|
HIBAG.Rcheck/tests_i386/runTests.Rout
R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> #############################################################
> #
> # DESCRIPTION: Unit tests in the HIBAG package
> #
>
> # load the HIBAG package
> library(HIBAG)
HIBAG (HLA Genotype Imputation with Attribute Bagging)
Kernel Version: v1.4
>
>
> #############################################################
>
> # a list of HLA genes
> hla.list <- c("A", "B", "C", "DQA1", "DQB1", "DRB1")
>
> # pre-defined lower bound of prediction accuracy
> hla.acc <- c(0.9, 0.8, 0.8, 0.85, 0.85, 0.75)
>
>
> for (hla.idx in seq_len(length(hla.list)))
+ {
+ hla.id <- hla.list[hla.idx]
+
+ # make a "hlaAlleleClass" object
+ hla <- hlaAllele(HLA_Type_Table$sample.id,
+ H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")],
+ H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")],
+ locus=hla.id, assembly="hg19")
+
+ # divide HLA types randomly
+ set.seed(100)
+ hlatab <- hlaSplitAllele(hla, train.prop=0.5)
+
+ # SNP predictors within the flanking region on each side
+ region <- 500 # kb
+ snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id,
+ HapMap_CEU_Geno$snp.position,
+ hla.id, region*1000, assembly="hg19")
+
+ # training and validation genotypes
+ train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
+ snp.sel=match(snpid, HapMap_CEU_Geno$snp.id),
+ samp.sel=match(hlatab$training$value$sample.id,
+ HapMap_CEU_Geno$sample.id))
+ test.geno <- hlaGenoSubset(HapMap_CEU_Geno,
+ samp.sel=match(hlatab$validation$value$sample.id,
+ HapMap_CEU_Geno$sample.id))
+
+
+ # train a HIBAG model
+ set.seed(100)
+ model <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=10)
+ summary(model)
+
+ # validation
+ pred <- predict(model, test.geno)
+ summary(pred)
+
+ # compare
+ comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
+ call.threshold=0)
+ print(comp$overall)
+
+ # check
+ if (comp$overall$acc.haplo < hla.acc[hla.idx])
+ {
+ stop("HLA - ", hla.id, ", 'acc.haplo' should be >= ",
+ hla.acc[hla.idx], ".")
+ }
+
+ cat("\n\n")
+ }
Exclude 9 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:17
[1] 2019-04-13 03:03:18, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 42
[2] 2019-04-13 03:03:18, OOB Acc: 92.31%, # of SNPs: 19, # of Haplo: 38
[3] 2019-04-13 03:03:18, OOB Acc: 86.36%, # of SNPs: 12, # of Haplo: 21
[4] 2019-04-13 03:03:18, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 44
[5] 2019-04-13 03:03:18, OOB Acc: 100.00%, # of SNPs: 14, # of Haplo: 30
[6] 2019-04-13 03:03:18, OOB Acc: 85.71%, # of SNPs: 18, # of Haplo: 47
[7] 2019-04-13 03:03:18, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 32
[8] 2019-04-13 03:03:19, OOB Acc: 83.33%, # of SNPs: 17, # of Haplo: 56
[9] 2019-04-13 03:03:19, OOB Acc: 100.00%, # of SNPs: 18, # of Haplo: 44
[10] 2019-04-13 03:03:19, OOB Acc: 81.82%, # of SNPs: 14, # of Haplo: 31
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002811482 0.0003054155 0.0005238215 0.0037455050 0.0101502970 0.0181041748
Max. Mean SD
0.3808460815 0.0367397147 0.0874239077
Accuracy with training data: 98.5%
Out-of-bag accuracy: 89.9%
Gene: A
Training dataset: 34 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 10
total # of SNPs used: 92
avg. # of SNPs in an individual classifier: 15.60
(sd: 2.27, min: 12, max: 19, median: 15.00)
avg. # of haplotypes in an individual classifier: 38.50
(sd: 10.16, min: 21, max: 56, median: 40.00)
avg. out-of-bag accuracy: 89.89%
(sd: 6.29%, min: 81.82%, max: 100.00%, median: 89.20%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002811482 0.0003054155 0.0005238215 0.0037455050 0.0101502970 0.0181041748
Max. Mean SD
0.3808460815 0.0367397147 0.0874239077
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:19) 0%
Predicting (2019-04-13 03:03:19) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 4 (15.4%) 4 (15.4%) 17 (65.4%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0008583 0.0066451 0.0258049 0.0258304 0.3808461
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 26 25 51 0.9615385 0.9807692 0
n.call call.rate
1 26 1
Exclude 1 monomorphic SNP
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 340, # of samples: 28
# of unique HLA alleles: 22
[-] 2019-04-13 03:03:19
[1] 2019-04-13 03:03:20, OOB Acc: 75.00%, # of SNPs: 18, # of Haplo: 127
[2] 2019-04-13 03:03:21, OOB Acc: 72.73%, # of SNPs: 20, # of Haplo: 118
[3] 2019-04-13 03:03:21, OOB Acc: 60.00%, # of SNPs: 16, # of Haplo: 27
[4] 2019-04-13 03:03:21, OOB Acc: 70.83%, # of SNPs: 16, # of Haplo: 94
[5] 2019-04-13 03:03:21, OOB Acc: 63.64%, # of SNPs: 11, # of Haplo: 39
[6] 2019-04-13 03:03:21, OOB Acc: 81.25%, # of SNPs: 18, # of Haplo: 97
[7] 2019-04-13 03:03:21, OOB Acc: 55.56%, # of SNPs: 13, # of Haplo: 52
[8] 2019-04-13 03:03:22, OOB Acc: 83.33%, # of SNPs: 20, # of Haplo: 84
[9] 2019-04-13 03:03:22, OOB Acc: 45.45%, # of SNPs: 10, # of Haplo: 16
[10] 2019-04-13 03:03:22, OOB Acc: 73.08%, # of SNPs: 15, # of Haplo: 71
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001475023 0.0001478264 0.0001507439 0.0027105247 0.0048336862 0.0125263769
Max. Mean SD
0.0738641615 0.0109592146 0.0155234908
Accuracy with training data: 94.6%
Out-of-bag accuracy: 68.1%
Gene: B
Training dataset: 28 samples X 340 SNPs
# of HLA alleles: 22
# of individual classifiers: 10
total # of SNPs used: 98
avg. # of SNPs in an individual classifier: 15.70
(sd: 3.50, min: 10, max: 20, median: 16.00)
avg. # of haplotypes in an individual classifier: 72.50
(sd: 38.07, min: 16, max: 127, median: 77.50)
avg. out-of-bag accuracy: 68.09%
(sd: 11.84%, min: 45.45%, max: 83.33%, median: 71.78%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001475023 0.0001478264 0.0001507439 0.0027105247 0.0048336862 0.0125263769
Max. Mean SD
0.0738641615 0.0109592146 0.0155234908
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 340 SNPs, 22 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 15
Predicting (2019-04-13 03:03:22) 0%
Predicting (2019-04-13 03:03:22) 100%
Gene: B
Range: [31321649bp, 31324989bp] on hg19
# of samples: 15
# of unique HLA alleles: 9
# of unique HLA genotypes: 13
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
2 (13.3%) 6 (40.0%) 3 (20.0%) 4 (26.7%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000e-08 5.102e-04 2.615e-03 1.161e-02 9.485e-03 8.519e-02
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 15 12 27 0.8 0.9 0
n.call call.rate
1 15 1
Exclude 2 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 354, # of samples: 36
# of unique HLA alleles: 17
[-] 2019-04-13 03:03:22
[1] 2019-04-13 03:03:22, OOB Acc: 96.15%, # of SNPs: 19, # of Haplo: 35
[2] 2019-04-13 03:03:23, OOB Acc: 90.91%, # of SNPs: 25, # of Haplo: 75
[3] 2019-04-13 03:03:23, OOB Acc: 66.67%, # of SNPs: 12, # of Haplo: 42
[4] 2019-04-13 03:03:23, OOB Acc: 96.43%, # of SNPs: 22, # of Haplo: 32
[5] 2019-04-13 03:03:23, OOB Acc: 83.33%, # of SNPs: 18, # of Haplo: 42
[6] 2019-04-13 03:03:23, OOB Acc: 76.67%, # of SNPs: 17, # of Haplo: 40
[7] 2019-04-13 03:03:23, OOB Acc: 88.46%, # of SNPs: 20, # of Haplo: 44
[8] 2019-04-13 03:03:23, OOB Acc: 67.86%, # of SNPs: 17, # of Haplo: 25
[9] 2019-04-13 03:03:24, OOB Acc: 86.36%, # of SNPs: 19, # of Haplo: 50
[10] 2019-04-13 03:03:24, OOB Acc: 100.00%, # of SNPs: 21, # of Haplo: 75
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0006082516 0.0006183124 0.0007088597 0.0014283040 0.0034803366 0.0089095913
Max. Mean SD
0.0574239572 0.0093779653 0.0130718885
Accuracy with training data: 100.0%
Out-of-bag accuracy: 85.3%
Gene: C
Training dataset: 36 samples X 354 SNPs
# of HLA alleles: 17
# of individual classifiers: 10
total # of SNPs used: 114
avg. # of SNPs in an individual classifier: 19.00
(sd: 3.46, min: 12, max: 25, median: 19.00)
avg. # of haplotypes in an individual classifier: 46.00
(sd: 16.76, min: 25, max: 75, median: 42.00)
avg. out-of-bag accuracy: 85.28%
(sd: 11.69%, min: 66.67%, max: 100.00%, median: 87.41%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0006082516 0.0006183124 0.0007088597 0.0014283040 0.0034803366 0.0089095913
Max. Mean SD
0.0574239572 0.0093779653 0.0130718885
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 354 SNPs, 17 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 24
Predicting (2019-04-13 03:03:24) 0%
Predicting (2019-04-13 03:03:24) 100%
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 24
# of unique HLA alleles: 14
# of unique HLA genotypes: 16
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
3 (12.5%) 4 (16.7%) 7 (29.2%) 10 (41.7%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0001148 0.0005418 0.0056976 0.0069050 0.0317705
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 24 17 41 0.7083333 0.8541667 0
n.call call.rate
1 24 1
Exclude 6 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 343, # of samples: 31
# of unique HLA alleles: 7
[-] 2019-04-13 03:03:24
[1] 2019-04-13 03:03:24, OOB Acc: 86.36%, # of SNPs: 10, # of Haplo: 19
[2] 2019-04-13 03:03:24, OOB Acc: 92.31%, # of SNPs: 12, # of Haplo: 37
[3] 2019-04-13 03:03:24, OOB Acc: 86.36%, # of SNPs: 12, # of Haplo: 21
[4] 2019-04-13 03:03:24, OOB Acc: 95.00%, # of SNPs: 10, # of Haplo: 17
[5] 2019-04-13 03:03:24, OOB Acc: 88.89%, # of SNPs: 11, # of Haplo: 31
[6] 2019-04-13 03:03:24, OOB Acc: 100.00%, # of SNPs: 14, # of Haplo: 39
[7] 2019-04-13 03:03:24, OOB Acc: 90.00%, # of SNPs: 9, # of Haplo: 16
[8] 2019-04-13 03:03:24, OOB Acc: 95.45%, # of SNPs: 10, # of Haplo: 10
[9] 2019-04-13 03:03:24, OOB Acc: 83.33%, # of SNPs: 7, # of Haplo: 9
[10] 2019-04-13 03:03:24, OOB Acc: 87.50%, # of SNPs: 7, # of Haplo: 17
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.002003247 0.002016202 0.002132800 0.009847689 0.018023446 0.040449819
Max. Mean SD
0.477353285 0.059978120 0.120992882
Accuracy with training data: 95.2%
Out-of-bag accuracy: 90.5%
Gene: DQA1
Training dataset: 31 samples X 343 SNPs
# of HLA alleles: 7
# of individual classifiers: 10
total # of SNPs used: 72
avg. # of SNPs in an individual classifier: 10.20
(sd: 2.20, min: 7, max: 14, median: 10.00)
avg. # of haplotypes in an individual classifier: 21.60
(sd: 10.55, min: 9, max: 39, median: 18.00)
avg. out-of-bag accuracy: 90.52%
(sd: 5.12%, min: 83.33%, max: 100.00%, median: 89.44%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.002003247 0.002016202 0.002132800 0.009847689 0.018023446 0.040449819
Max. Mean SD
0.477353285 0.059978120 0.120992882
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 343 SNPs, 7 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 29
Predicting (2019-04-13 03:03:24) 0%
Predicting (2019-04-13 03:03:24) 100%
Gene: DQA1
Range: [32605169bp, 32612152bp] on hg19
# of samples: 29
# of unique HLA alleles: 7
# of unique HLA genotypes: 16
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.4%) 7 (24.1%) 5 (17.2%) 16 (55.2%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0007125 0.0034788 0.0080649 0.0285052 0.0159776 0.4784200
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 29 23 51 0.7931034 0.8793103 0
n.call call.rate
1 29 1
Exclude 5 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 351, # of samples: 34
# of unique HLA alleles: 12
[-] 2019-04-13 03:03:24
[1] 2019-04-13 03:03:24, OOB Acc: 81.82%, # of SNPs: 17, # of Haplo: 58
[2] 2019-04-13 03:03:25, OOB Acc: 76.92%, # of SNPs: 13, # of Haplo: 45
[3] 2019-04-13 03:03:25, OOB Acc: 86.67%, # of SNPs: 15, # of Haplo: 56
[4] 2019-04-13 03:03:25, OOB Acc: 78.57%, # of SNPs: 14, # of Haplo: 16
[5] 2019-04-13 03:03:25, OOB Acc: 89.29%, # of SNPs: 17, # of Haplo: 35
[6] 2019-04-13 03:03:25, OOB Acc: 86.36%, # of SNPs: 11, # of Haplo: 49
[7] 2019-04-13 03:03:25, OOB Acc: 79.17%, # of SNPs: 13, # of Haplo: 33
[8] 2019-04-13 03:03:25, OOB Acc: 92.31%, # of SNPs: 16, # of Haplo: 35
[9] 2019-04-13 03:03:25, OOB Acc: 91.67%, # of SNPs: 22, # of Haplo: 81
[10] 2019-04-13 03:03:26, OOB Acc: 87.50%, # of SNPs: 21, # of Haplo: 45
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0003081898 0.0003364790 0.0005910815 0.0036371930 0.0059946892 0.0156499571
Max. Mean SD
0.4243764595 0.0464079969 0.1113675480
Accuracy with training data: 94.1%
Out-of-bag accuracy: 85.0%
Gene: DQB1
Training dataset: 34 samples X 351 SNPs
# of HLA alleles: 12
# of individual classifiers: 10
total # of SNPs used: 98
avg. # of SNPs in an individual classifier: 15.90
(sd: 3.51, min: 11, max: 22, median: 15.50)
avg. # of haplotypes in an individual classifier: 45.30
(sd: 17.59, min: 16, max: 81, median: 45.00)
avg. out-of-bag accuracy: 85.03%
(sd: 5.56%, min: 76.92%, max: 92.31%, median: 86.52%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0003081898 0.0003364790 0.0005910815 0.0036371930 0.0059946892 0.0156499571
Max. Mean SD
0.4243764595 0.0464079969 0.1113675480
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 351 SNPs, 12 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:26) 0%
Predicting (2019-04-13 03:03:26) 100%
Gene: DQB1
Range: [32627241bp, 32634466bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 18
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
2 (7.7%) 6 (23.1%) 4 (15.4%) 14 (53.8%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
8.000e-09 3.339e-04 2.401e-03 3.725e-03 4.974e-03 1.488e-02
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 26 23 48 0.8846154 0.9230769 0
n.call call.rate
1 26 1
Exclude 4 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 18
# of SNPs: 323, # of samples: 35
# of unique HLA alleles: 20
[-] 2019-04-13 03:03:26
[1] 2019-04-13 03:03:26, OOB Acc: 70.00%, # of SNPs: 19, # of Haplo: 83
[2] 2019-04-13 03:03:26, OOB Acc: 81.25%, # of SNPs: 18, # of Haplo: 102
[3] 2019-04-13 03:03:27, OOB Acc: 76.67%, # of SNPs: 20, # of Haplo: 138
[4] 2019-04-13 03:03:27, OOB Acc: 95.00%, # of SNPs: 16, # of Haplo: 65
[5] 2019-04-13 03:03:27, OOB Acc: 60.00%, # of SNPs: 16, # of Haplo: 78
[6] 2019-04-13 03:03:28, OOB Acc: 60.00%, # of SNPs: 15, # of Haplo: 63
[7] 2019-04-13 03:03:28, OOB Acc: 77.27%, # of SNPs: 19, # of Haplo: 71
[8] 2019-04-13 03:03:28, OOB Acc: 61.54%, # of SNPs: 12, # of Haplo: 27
[9] 2019-04-13 03:03:28, OOB Acc: 75.00%, # of SNPs: 15, # of Haplo: 54
[10] 2019-04-13 03:03:29, OOB Acc: 66.67%, # of SNPs: 20, # of Haplo: 60
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0003877617 0.0003912266 0.0004224100 0.0014197688 0.0026546695 0.0060312073
Max. Mean SD
0.4385472809 0.0275068316 0.0991274560
Accuracy with training data: 94.3%
Out-of-bag accuracy: 72.3%
Gene: DRB1
Training dataset: 35 samples X 323 SNPs
# of HLA alleles: 20
# of individual classifiers: 10
total # of SNPs used: 118
avg. # of SNPs in an individual classifier: 17.00
(sd: 2.62, min: 12, max: 20, median: 17.00)
avg. # of haplotypes in an individual classifier: 74.10
(sd: 29.80, min: 27, max: 138, median: 68.00)
avg. out-of-bag accuracy: 72.34%
(sd: 11.06%, min: 60.00%, max: 95.00%, median: 72.50%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0003877617 0.0003912266 0.0004224100 0.0014197688 0.0026546695 0.0060312073
Max. Mean SD
0.4385472809 0.0275068316 0.0991274560
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 323 SNPs, 20 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 25
Predicting (2019-04-13 03:03:29) 0%
Predicting (2019-04-13 03:03:29) 100%
Gene: DRB1
Range: [32546546bp, 32557613bp] on hg19
# of samples: 25
# of unique HLA alleles: 9
# of unique HLA genotypes: 16
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
4 (16.0%) 8 (32.0%) 7 (28.0%) 6 (24.0%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0000408 0.0005342 0.0269892 0.0015067 0.4388277
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 25 15 38 0.6 0.76 0
n.call call.rate
1 25 1
>
>
>
> #############################################################
>
> {
+ function.list <- readRDS(
+ system.file("Meta", "Rd.rds", package="HIBAG"))$Name
+
+ sapply(function.list, FUN = function(func.name)
+ {
+ args <- list(
+ topic = func.name,
+ package = "HIBAG",
+ echo = FALSE,
+ verbose = FALSE,
+ ask = FALSE
+ )
+ suppressWarnings(do.call(example, args))
+ NULL
+ })
+ invisible()
+ }
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 24
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:29
1, SNP: 155, Loss: 186.198, OOB Acc: 68.18%, # of Haplo: 13
2, SNP: 111, Loss: 138.902, OOB Acc: 72.73%, # of Haplo: 13
3, SNP: 159, Loss: 134.624, OOB Acc: 81.82%, # of Haplo: 15
4, SNP: 75, Loss: 120.53, OOB Acc: 86.36%, # of Haplo: 16
5, SNP: 129, Loss: 79.3436, OOB Acc: 90.91%, # of Haplo: 20
6, SNP: 135, Loss: 56.5856, OOB Acc: 90.91%, # of Haplo: 20
7, SNP: 162, Loss: 40.1347, OOB Acc: 90.91%, # of Haplo: 20
8, SNP: 192, Loss: 29.7565, OOB Acc: 90.91%, # of Haplo: 24
9, SNP: 182, Loss: 23.1586, OOB Acc: 90.91%, # of Haplo: 24
10, SNP: 24, Loss: 16.7007, OOB Acc: 90.91%, # of Haplo: 27
11, SNP: 154, Loss: 15.6368, OOB Acc: 90.91%, # of Haplo: 28
12, SNP: 17, Loss: 15.4059, OOB Acc: 90.91%, # of Haplo: 39
13, SNP: 199, Loss: 14.4894, OOB Acc: 90.91%, # of Haplo: 40
14, SNP: 73, Loss: 8.03154, OOB Acc: 90.91%, # of Haplo: 42
15, SNP: 151, Loss: 5.75619, OOB Acc: 90.91%, # of Haplo: 42
[1] 2019-04-13 03:03:29, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 42
1, SNP: 87, Loss: 163.788, OOB Acc: 50.00%, # of Haplo: 15
2, SNP: 179, Loss: 159.036, OOB Acc: 57.69%, # of Haplo: 16
3, SNP: 162, Loss: 127.744, OOB Acc: 61.54%, # of Haplo: 17
4, SNP: 113, Loss: 120.348, OOB Acc: 76.92%, # of Haplo: 17
5, SNP: 149, Loss: 99.7027, OOB Acc: 80.77%, # of Haplo: 19
6, SNP: 183, Loss: 63.9201, OOB Acc: 80.77%, # of Haplo: 19
7, SNP: 82, Loss: 46.9454, OOB Acc: 80.77%, # of Haplo: 19
8, SNP: 73, Loss: 36.2707, OOB Acc: 84.62%, # of Haplo: 21
9, SNP: 47, Loss: 34.9876, OOB Acc: 84.62%, # of Haplo: 22
10, SNP: 140, Loss: 27.9494, OOB Acc: 84.62%, # of Haplo: 22
11, SNP: 189, Loss: 23.9141, OOB Acc: 84.62%, # of Haplo: 23
12, SNP: 249, Loss: 18.884, OOB Acc: 84.62%, # of Haplo: 27
13, SNP: 151, Loss: 15.8511, OOB Acc: 84.62%, # of Haplo: 27
14, SNP: 21, Loss: 11.1982, OOB Acc: 88.46%, # of Haplo: 30
15, SNP: 215, Loss: 10.7762, OOB Acc: 88.46%, # of Haplo: 32
16, SNP: 136, Loss: 10.5472, OOB Acc: 88.46%, # of Haplo: 38
17, SNP: 117, Loss: 10.3045, OOB Acc: 88.46%, # of Haplo: 38
18, SNP: 106, Loss: 10.3041, OOB Acc: 92.31%, # of Haplo: 38
19, SNP: 199, Loss: 9.5325, OOB Acc: 92.31%, # of Haplo: 38
[2] 2019-04-13 03:03:29, OOB Acc: 92.31%, # of SNPs: 19, # of Haplo: 38
1, SNP: 135, Loss: 156.001, OOB Acc: 50.00%, # of Haplo: 13
2, SNP: 141, Loss: 121.763, OOB Acc: 59.09%, # of Haplo: 14
3, SNP: 162, Loss: 110.134, OOB Acc: 63.64%, # of Haplo: 14
4, SNP: 73, Loss: 95.625, OOB Acc: 72.73%, # of Haplo: 15
5, SNP: 96, Loss: 87.0485, OOB Acc: 86.36%, # of Haplo: 16
6, SNP: 129, Loss: 49.5862, OOB Acc: 86.36%, # of Haplo: 18
7, SNP: 147, Loss: 27.5253, OOB Acc: 86.36%, # of Haplo: 19
8, SNP: 105, Loss: 16.9538, OOB Acc: 86.36%, # of Haplo: 19
9, SNP: 258, Loss: 11.9891, OOB Acc: 86.36%, # of Haplo: 19
10, SNP: 160, Loss: 10.5954, OOB Acc: 86.36%, # of Haplo: 20
11, SNP: 199, Loss: 9.81573, OOB Acc: 86.36%, # of Haplo: 20
12, SNP: 151, Loss: 9.3333, OOB Acc: 86.36%, # of Haplo: 21
[3] 2019-04-13 03:03:29, OOB Acc: 86.36%, # of SNPs: 12, # of Haplo: 21
1, SNP: 184, Loss: 221.947, OOB Acc: 63.64%, # of Haplo: 15
2, SNP: 87, Loss: 159.682, OOB Acc: 77.27%, # of Haplo: 18
3, SNP: 84, Loss: 135.191, OOB Acc: 81.82%, # of Haplo: 21
4, SNP: 36, Loss: 128.841, OOB Acc: 86.36%, # of Haplo: 23
5, SNP: 109, Loss: 94.3071, OOB Acc: 90.91%, # of Haplo: 27
6, SNP: 130, Loss: 66.1551, OOB Acc: 90.91%, # of Haplo: 29
7, SNP: 64, Loss: 50.915, OOB Acc: 90.91%, # of Haplo: 29
8, SNP: 194, Loss: 41.1566, OOB Acc: 90.91%, # of Haplo: 29
9, SNP: 193, Loss: 32.6689, OOB Acc: 90.91%, # of Haplo: 30
10, SNP: 123, Loss: 30.6255, OOB Acc: 90.91%, # of Haplo: 30
11, SNP: 55, Loss: 30.1894, OOB Acc: 90.91%, # of Haplo: 35
12, SNP: 95, Loss: 29.8967, OOB Acc: 90.91%, # of Haplo: 43
13, SNP: 151, Loss: 25.6379, OOB Acc: 90.91%, # of Haplo: 43
14, SNP: 199, Loss: 24.4761, OOB Acc: 90.91%, # of Haplo: 44
[4] 2019-04-13 03:03:29, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 44
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002704645 0.0002740287 0.0003061065 0.0041563208 0.0120425968 0.0259547286
Max. Mean SD
0.3993959563 0.0433199485 0.0939557543
Accuracy with training data: 98.5%
Out-of-bag accuracy: 90.1%
Gene: A
Training dataset: 34 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 47
avg. # of SNPs in an individual classifier: 15.00
(sd: 2.94, min: 12, max: 19, median: 14.50)
avg. # of haplotypes in an individual classifier: 36.25
(sd: 10.47, min: 21, max: 44, median: 40.00)
avg. out-of-bag accuracy: 90.12%
(sd: 2.59%, min: 86.36%, max: 92.31%, median: 90.91%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002704645 0.0002740287 0.0003061065 0.0041563208 0.0120425968 0.0259547286
Max. Mean SD
0.3993959563 0.0433199485 0.0939557543
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:29) 0%
Predicting (2019-04-13 03:03:29) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 1 (3.8%) 8 (30.8%) 16 (61.5%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0009577 0.0048947 0.0311502 0.0380943 0.3993960
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:29) 0%
Predicting (2019-04-13 03:03:29) 100%
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 90
Predicting (2019-04-13 03:03:29) 0%
Predicting (2019-04-13 03:03:29) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
using the default genome assembly (assembly="hg19")
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 12
# of unique HLA genotypes: 28
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 100
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
dominant model:
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042*
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000
02:01 25 35 52.0 48.6 0.0000 1.000 1.000
02:06 59 1 50.8 0.0 0.0000 1.000 1.000
03:01 51 9 49.0 55.6 0.0000 1.000 1.000
11:01 55 5 50.9 40.0 0.0000 1.000 1.000
23:01 58 2 50.0 50.0 0.0000 1.000 1.000
24:03 59 1 50.8 0.0 0.0000 1.000 1.000
25:01 55 5 52.7 20.0 0.8727 0.350 0.353
26:01 57 3 52.6 0.0 1.4035 0.236 0.237
29:02 56 4 51.8 25.0 0.2679 0.605 0.612
31:01 57 3 49.1 66.7 0.0000 1.000 1.000
32:01 56 4 46.4 100.0 2.4107 0.121 0.112
68:01 57 3 52.6 0.0 1.4035 0.236 0.237
additive model:
[-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p
01:01 95 25 50.5 48.0 0.0000 1.000 1.000
02:01 77 43 48.1 53.5 0.1450 0.703 0.704
02:06 119 1 50.4 0.0 0.0000 1.000 1.000
03:01 111 9 49.5 55.6 0.0000 1.000 1.000
11:01 115 5 50.4 40.0 0.0000 1.000 1.000
23:01 117 3 50.4 33.3 0.0000 1.000 1.000
24:02 109 11 46.8 81.8 3.6030 0.058 0.053
24:03 119 1 50.4 0.0 0.0000 1.000 1.000
25:01 115 5 51.3 20.0 0.8348 0.361 0.364
26:01 117 3 51.3 0.0 1.3675 0.242 0.244
29:02 116 4 50.9 25.0 0.2586 0.611 0.619
31:01 117 3 49.6 66.7 0.0000 1.000 1.000
32:01 116 4 48.3 100.0 2.3276 0.127 0.119
68:01 117 3 51.3 0.0 1.3675 0.242 0.244
recessive model:
[-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p
01:01 59 1 50.8 0 0.000 1.000 1.000
02:01 52 8 46.2 75 1.298 0.255 0.254
02:06 60 0 50.0 . . . .
03:01 60 0 50.0 . . . .
11:01 60 0 50.0 . . . .
23:01 59 1 50.8 0 0.000 1.000 1.000
24:02 60 0 50.0 . . . .
24:03 60 0 50.0 . . . .
25:01 60 0 50.0 . . . .
26:01 60 0 50.0 . . . .
29:02 60 0 50.0 . . . .
31:01 60 0 50.0 . . . .
32:01 60 0 50.0 . . . .
68:01 60 0 50.0 . . . .
genotype model:
[-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042*
-----
01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000
02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271
02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000
11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000
23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000
24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353
26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612
31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000
32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112
68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
dominant model:
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p
01:01 36 24 -0.14684 -0.117427 0.909
02:01 25 35 -0.32331 -0.000618 0.190
02:06 59 1 -0.14024 0.170057 .
03:01 51 9 -0.05600 -0.583178 0.147
11:01 55 5 -0.19188 0.489815 0.287
23:01 58 2 -0.15400 0.413687 0.281
24:02 49 11 -0.10486 -0.269664 0.537
24:03 59 1 -0.11409 -1.373118 .
25:01 55 5 -0.12237 -0.274749 0.742
26:01 57 3 -0.12473 -0.331558 0.690
29:02 56 4 -0.13044 -0.199941 0.789
31:01 57 3 -0.10097 -0.783003 0.607
32:01 56 4 -0.07702 -0.947791 0.092
68:01 57 3 -0.16915 0.512457 0.196
genotype model:
[-/-] [-/h] [h/h] avg.[-/-] avg.[-/h] avg.[h/h] anova.p
01:01 36 23 1 -0.14684 -0.08833 -0.78655 0.784
02:01 25 27 8 -0.32331 -0.02341 0.07631 0.446
02:06 59 1 0 -0.14024 0.17006 . 0.756
03:01 51 9 0 -0.05600 -0.58318 . 0.138
11:01 55 5 0 -0.19188 0.48981 . 0.137
23:01 58 1 1 -0.15400 0.10762 0.71975 0.663
24:02 49 11 0 -0.10486 -0.26966 . 0.618
24:03 59 1 0 -0.11409 -1.37312 . 0.205
25:01 55 5 0 -0.12237 -0.27475 . 0.742
26:01 57 3 0 -0.12473 -0.33156 . 0.725
29:02 56 4 0 -0.13044 -0.19994 . 0.892
31:01 57 3 0 -0.10097 -0.78300 . 0.243
32:01 56 4 0 -0.07702 -0.94779 . 0.086
68:01 57 3 0 -0.16915 0.51246 . 0.243
Logistic regression (dominant model) with 60 individuals:
glm(case ˜ h, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.792e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 4.657e-17
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.372e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.624e-01
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.418e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 2.686e-16
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.495e+00
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.170e+00
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.282e-01
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.771e+01
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
h.2.5% h.97.5% h.pval
24:02 0.1585 3.4251 0.032*
-----
01:01 -1.0330 1.0330 1.000
02:01 -1.1643 0.8899 0.793
02:06 -2868.1268 2836.9268 0.991
03:01 -1.1624 1.6872 0.718
11:01 -2.3074 1.4237 0.643
23:01 -2.8192 2.8192 1.000
24:03 -2868.1268 2836.9268 0.991
25:01 -3.7498 0.7588 0.194
26:01 -2731.9621 2698.6192 0.990
29:02 -3.4931 1.1530 0.324
31:01 -1.7277 3.1842 0.561
32:01 -3859.2763 3894.6947 0.993
68:01 -2731.9621 2698.6192 0.990
Logistic regression (dominant model) with 60 individuals:
glm(case ˜ h + pc1, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.793e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -2.268e-04
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.370e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.562e+01
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.686e-01
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.451e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 -3.062e-03
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.501e+00
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.189e+00
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.289e-01
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.781e+01
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.673e+01
h.2.5% h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02 0.1587 3.4264 0.032* 0.011111 -0.5249 0.5471 0.968
-----
01:01 -1.0334 1.0330 1.000 -0.005807 -0.5126 0.5010 0.982
02:01 -1.1652 0.8913 0.794 -0.002618 -0.5102 0.5049 0.992
02:06 -2868.1460 2836.9076 0.991 -0.028534 -0.5374 0.4803 0.912
03:01 -1.1813 1.7185 0.717 0.011958 -0.5044 0.5283 0.964
11:01 -2.3225 1.4322 0.642 0.008025 -0.5026 0.5186 0.975
23:01 -2.8348 2.8287 0.998 -0.005857 -0.5148 0.5031 0.982
24:03 -2868.1286 2836.9250 0.991 -0.011249 -0.5182 0.4957 0.965
25:01 -3.7579 0.7568 0.193 -0.025685 -0.5490 0.4976 0.923
26:01 -2731.8901 2698.5450 0.990 -0.014069 -0.5297 0.5015 0.957
29:02 -3.5309 1.1526 0.320 0.033234 -0.4796 0.5461 0.899
31:01 -1.7274 3.1851 0.561 -0.008320 -0.5153 0.4987 0.974
32:01 -3845.6317 3881.2510 0.993 -0.125426 -0.6671 0.4162 0.650
68:01 -2721.2124 2687.7497 0.990 -0.086589 -0.6512 0.4781 0.764
Logistic regression (dominant model) with 60 individuals:
glm(case ˜ h + pc1, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est_OR
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 6.005e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 9.998e-01
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 8.720e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 1.647e-07
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 1.308e+00
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 6.407e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 9.969e-01
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 1.676e-07
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 2.230e-01
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.744e-08
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 3.045e-01
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 2.073e+00
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 5.428e+07
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.416e-08
h.2.5%_OR h.97.5%_OR h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02 1.17200 30.766 0.032* 0.011111 -0.5249 0.5471 0.968
-----
01:01 0.35579 2.809 1.000 -0.005807 -0.5126 0.5010 0.982
02:01 0.31185 2.438 0.794 -0.002618 -0.5102 0.5049 0.992
02:06 0.00000 Inf 0.991 -0.028534 -0.5374 0.4803 0.912
03:01 0.30687 5.576 0.717 0.011958 -0.5044 0.5283 0.964
11:01 0.09803 4.188 0.642 0.008025 -0.5026 0.5186 0.975
23:01 0.05873 16.923 0.998 -0.005857 -0.5148 0.5031 0.982
24:03 0.00000 Inf 0.991 -0.011249 -0.5182 0.4957 0.965
25:01 0.02333 2.131 0.193 -0.025685 -0.5490 0.4976 0.923
26:01 0.00000 Inf 0.990 -0.014069 -0.5297 0.5015 0.957
29:02 0.02928 3.167 0.320 0.033234 -0.4796 0.5461 0.899
31:01 0.17774 24.171 0.561 -0.008320 -0.5153 0.4987 0.974
32:01 0.00000 Inf 0.993 -0.125426 -0.6671 0.4162 0.650
68:01 0.00000 Inf 0.990 -0.086589 -0.6512 0.4781 0.764
Linear regression (dominant model) with 60 individuals:
glm(y ˜ h, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5% h.97.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.02941 -0.4805 0.5393
02:01 25 35 -0.32331 -0.000618 0.190 0.32269 -0.1772 0.8226
02:06 59 1 -0.14024 0.170057 . 0.31030 -1.6397 2.2603
03:01 51 9 -0.05600 -0.583178 0.147 -0.52718 -1.2136 0.1592
11:01 55 5 -0.19188 0.489815 0.287 0.68170 -0.2051 1.5685
23:01 58 2 -0.15400 0.413687 0.281 0.56768 -0.8165 1.9518
24:02 49 11 -0.10486 -0.269664 0.537 -0.16481 -0.8091 0.4795
24:03 59 1 -0.11409 -1.373118 . -1.25903 -3.1835 0.6655
25:01 55 5 -0.12237 -0.274749 0.742 -0.15237 -1.0555 0.7507
26:01 57 3 -0.12473 -0.331558 0.690 -0.20683 -1.3519 0.9383
29:02 56 4 -0.13044 -0.199941 0.789 -0.06950 -1.0709 0.9319
31:01 57 3 -0.10097 -0.783003 0.607 -0.68203 -1.8149 0.4508
32:01 56 4 -0.07702 -0.947791 0.092 -0.87077 -1.8470 0.1054
68:01 57 3 -0.16915 0.512457 0.196 0.68161 -0.4512 1.8145
h.pval
01:01 0.910
02:01 0.211
02:06 0.756
03:01 0.138
11:01 0.137
23:01 0.425
24:02 0.618
24:03 0.205
25:01 0.742
26:01 0.725
29:02 0.892
31:01 0.243
32:01 0.086
68:01 0.243
Linear regression (dominant model) with 60 individuals:
glm(y ˜ h + pc1, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773
02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891
02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722
03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884
11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520
23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598
24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034
24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708
25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388
26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460
29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496
31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254
32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862
68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749
h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386
02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414
02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371
03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539
11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474
23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343
24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393
24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395
25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396
26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390
29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372
31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371
32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210
68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294
Linear regression (dominant model) with 60 individuals:
glm(y ˜ h + pc1, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773
02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891
02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722
03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884
11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520
23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598
24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034
24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708
25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388
26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460
29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496
31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254
32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862
68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749
h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386
02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414
02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371
03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539
11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474
23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343
24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393
24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395
25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396
26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390
29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372
31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371
32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210
68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294
Logistic regression (additive model) with 60 individuals:
glm(case ˜ h, family = binomial, data = data)
[-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p h.est h.2.5%
24:02 109 11 46.8 81.8 3.6030 0.058 0.053 1.7918 0.1585
-----
01:01 95 25 50.5 48.0 0.0000 1.000 1.000 -0.1207 -1.0843
02:01 77 43 48.1 53.5 0.1450 0.703 0.704 0.2137 -0.5289
02:06 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268
03:01 111 9 49.5 55.6 0.0000 1.000 1.000 0.2624 -1.1624
11:01 115 5 50.4 40.0 0.0000 1.000 1.000 -0.4418 -2.3074
23:01 117 3 50.4 33.3 0.0000 1.000 1.000 -0.4323 -2.3435
24:03 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268
25:01 115 5 51.3 20.0 0.8348 0.361 0.364 -1.4955 -3.7498
26:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621
29:02 116 4 50.9 25.0 0.2586 0.611 0.619 -1.1701 -3.4931
31:01 117 3 49.6 66.7 0.0000 1.000 1.000 0.7282 -1.7277
32:01 116 4 48.3 100.0 2.3276 0.127 0.119 17.7092 -3859.2763
68:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621
h.97.5% h.pval
24:02 3.4251 0.032*
-----
01:01 0.8430 0.806
02:01 0.9563 0.573
02:06 2836.9268 0.991
03:01 1.6872 0.718
11:01 1.4237 0.643
23:01 1.4789 0.658
24:03 2836.9268 0.991
25:01 0.7588 0.194
26:01 2698.6192 0.990
29:02 1.1530 0.324
31:01 3.1842 0.561
32:01 3894.6947 0.993
68:01 2698.6192 0.990
Logistic regression (recessive model) with 60 individuals:
glm(case ˜ h, family = binomial, data = data)
[-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p h.est
01:01 59 1 50.8 0 0.000 1.000 1.000 -15.600
02:01 52 8 46.2 75 1.298 0.255 0.254 1.253
02:06 60 0 50.0 . . . . .
03:01 60 0 50.0 . . . . .
11:01 60 0 50.0 . . . . .
23:01 59 1 50.8 0 0.000 1.000 1.000 -15.600
24:02 60 0 50.0 . . . . .
24:03 60 0 50.0 . . . . .
25:01 60 0 50.0 . . . . .
26:01 60 0 50.0 . . . . .
29:02 60 0 50.0 . . . . .
31:01 60 0 50.0 . . . . .
32:01 60 0 50.0 . . . . .
68:01 60 0 50.0 . . . . .
h.2.5% h.97.5% h.pval
01:01 -2868.1268 2836.927 0.991
02:01 -0.4379 2.943 0.146
02:06 . . .
03:01 . . .
11:01 . . .
23:01 -2868.1268 2836.927 0.991
24:02 . . .
24:03 . . .
25:01 . . .
26:01 . . .
29:02 . . .
31:01 . . .
32:01 . . .
68:01 . . .
Logistic regression (genotype model) with 60 individuals:
glm(case ˜ h, family = binomial, data = data)
[-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042*
-----
01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000
02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271
02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000
11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000
23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000
24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353
26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612
31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000
32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112
68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
h1.est h1.2.5% h1.97.5% h1.pval h2.est h2.2.5% h2.97.5%
24:02 1.79176 0.1585 3.4251 0.032* . . .
-----
01:01 0.08701 -0.9600 1.1340 0.871 -15.566 -2868.0929 2836.961
02:01 -0.45474 -1.5524 0.6430 0.417 1.019 -0.7637 2.801
02:06 -15.59997 -2868.1268 2836.9268 0.991 . . .
03:01 0.26236 -1.1624 1.6872 0.718 . . .
11:01 -0.44183 -2.3074 1.4237 0.643 . . .
23:01 16.56607 -4686.4552 4719.5873 0.994 -16.566 -4719.5873 4686.455
24:03 -15.59997 -2868.1268 2836.9268 0.991 . . .
25:01 -1.49549 -3.7498 0.7588 0.194 . . .
26:01 -16.67143 -2731.9621 2698.6192 0.990 . . .
29:02 -1.17007 -3.4931 1.1530 0.324 . . .
31:01 0.72824 -1.7277 3.1842 0.561 . . .
32:01 17.70917 -3859.2763 3894.6947 0.993 . . .
68:01 -16.67143 -2731.9621 2698.6192 0.990 . . .
h2.pval
24:02 .
-----
01:01 0.991
02:01 0.263
02:06 .
03:01 .
11:01 .
23:01 0.994
24:03 .
25:01 .
26:01 .
29:02 .
31:01 .
32:01 .
68:01 .
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 24
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:30
1, SNP: 155, Loss: 186.198, OOB Acc: 68.18%, # of Haplo: 13
2, SNP: 111, Loss: 138.902, OOB Acc: 72.73%, # of Haplo: 13
3, SNP: 159, Loss: 134.624, OOB Acc: 81.82%, # of Haplo: 15
4, SNP: 75, Loss: 120.53, OOB Acc: 86.36%, # of Haplo: 16
5, SNP: 129, Loss: 79.3436, OOB Acc: 90.91%, # of Haplo: 20
6, SNP: 135, Loss: 56.5856, OOB Acc: 90.91%, # of Haplo: 20
7, SNP: 162, Loss: 40.1347, OOB Acc: 90.91%, # of Haplo: 20
8, SNP: 192, Loss: 29.7565, OOB Acc: 90.91%, # of Haplo: 24
9, SNP: 182, Loss: 23.1586, OOB Acc: 90.91%, # of Haplo: 24
10, SNP: 24, Loss: 16.7007, OOB Acc: 90.91%, # of Haplo: 27
11, SNP: 154, Loss: 15.6368, OOB Acc: 90.91%, # of Haplo: 28
12, SNP: 17, Loss: 15.4059, OOB Acc: 90.91%, # of Haplo: 39
13, SNP: 199, Loss: 14.4894, OOB Acc: 90.91%, # of Haplo: 40
14, SNP: 73, Loss: 8.03154, OOB Acc: 90.91%, # of Haplo: 42
15, SNP: 151, Loss: 5.75619, OOB Acc: 90.91%, # of Haplo: 42
[1] 2019-04-13 03:03:30, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 42
1, SNP: 87, Loss: 163.788, OOB Acc: 50.00%, # of Haplo: 15
2, SNP: 179, Loss: 159.036, OOB Acc: 57.69%, # of Haplo: 16
3, SNP: 162, Loss: 127.744, OOB Acc: 61.54%, # of Haplo: 17
4, SNP: 113, Loss: 120.348, OOB Acc: 76.92%, # of Haplo: 17
5, SNP: 149, Loss: 99.7027, OOB Acc: 80.77%, # of Haplo: 19
6, SNP: 183, Loss: 63.9201, OOB Acc: 80.77%, # of Haplo: 19
7, SNP: 82, Loss: 46.9454, OOB Acc: 80.77%, # of Haplo: 19
8, SNP: 73, Loss: 36.2707, OOB Acc: 84.62%, # of Haplo: 21
9, SNP: 47, Loss: 34.9876, OOB Acc: 84.62%, # of Haplo: 22
10, SNP: 140, Loss: 27.9494, OOB Acc: 84.62%, # of Haplo: 22
11, SNP: 189, Loss: 23.9141, OOB Acc: 84.62%, # of Haplo: 23
12, SNP: 249, Loss: 18.884, OOB Acc: 84.62%, # of Haplo: 27
13, SNP: 151, Loss: 15.8511, OOB Acc: 84.62%, # of Haplo: 27
14, SNP: 21, Loss: 11.1982, OOB Acc: 88.46%, # of Haplo: 30
15, SNP: 215, Loss: 10.7762, OOB Acc: 88.46%, # of Haplo: 32
16, SNP: 136, Loss: 10.5472, OOB Acc: 88.46%, # of Haplo: 38
17, SNP: 117, Loss: 10.3045, OOB Acc: 88.46%, # of Haplo: 38
18, SNP: 106, Loss: 10.3041, OOB Acc: 92.31%, # of Haplo: 38
19, SNP: 199, Loss: 9.5325, OOB Acc: 92.31%, # of Haplo: 38
[2] 2019-04-13 03:03:30, OOB Acc: 92.31%, # of SNPs: 19, # of Haplo: 38
1, SNP: 135, Loss: 156.001, OOB Acc: 50.00%, # of Haplo: 13
2, SNP: 141, Loss: 121.763, OOB Acc: 59.09%, # of Haplo: 14
3, SNP: 162, Loss: 110.134, OOB Acc: 63.64%, # of Haplo: 14
4, SNP: 73, Loss: 95.625, OOB Acc: 72.73%, # of Haplo: 15
5, SNP: 96, Loss: 87.0485, OOB Acc: 86.36%, # of Haplo: 16
6, SNP: 129, Loss: 49.5862, OOB Acc: 86.36%, # of Haplo: 18
7, SNP: 147, Loss: 27.5253, OOB Acc: 86.36%, # of Haplo: 19
8, SNP: 105, Loss: 16.9538, OOB Acc: 86.36%, # of Haplo: 19
9, SNP: 258, Loss: 11.9891, OOB Acc: 86.36%, # of Haplo: 19
10, SNP: 160, Loss: 10.5954, OOB Acc: 86.36%, # of Haplo: 20
11, SNP: 199, Loss: 9.81573, OOB Acc: 86.36%, # of Haplo: 20
12, SNP: 151, Loss: 9.3333, OOB Acc: 86.36%, # of Haplo: 21
[3] 2019-04-13 03:03:31, OOB Acc: 86.36%, # of SNPs: 12, # of Haplo: 21
1, SNP: 184, Loss: 221.947, OOB Acc: 63.64%, # of Haplo: 15
2, SNP: 87, Loss: 159.682, OOB Acc: 77.27%, # of Haplo: 18
3, SNP: 84, Loss: 135.191, OOB Acc: 81.82%, # of Haplo: 21
4, SNP: 36, Loss: 128.841, OOB Acc: 86.36%, # of Haplo: 23
5, SNP: 109, Loss: 94.3071, OOB Acc: 90.91%, # of Haplo: 27
6, SNP: 130, Loss: 66.1551, OOB Acc: 90.91%, # of Haplo: 29
7, SNP: 64, Loss: 50.915, OOB Acc: 90.91%, # of Haplo: 29
8, SNP: 194, Loss: 41.1566, OOB Acc: 90.91%, # of Haplo: 29
9, SNP: 193, Loss: 32.6689, OOB Acc: 90.91%, # of Haplo: 30
10, SNP: 123, Loss: 30.6255, OOB Acc: 90.91%, # of Haplo: 30
11, SNP: 55, Loss: 30.1894, OOB Acc: 90.91%, # of Haplo: 35
12, SNP: 95, Loss: 29.8967, OOB Acc: 90.91%, # of Haplo: 43
13, SNP: 151, Loss: 25.6379, OOB Acc: 90.91%, # of Haplo: 43
14, SNP: 199, Loss: 24.4761, OOB Acc: 90.91%, # of Haplo: 44
[4] 2019-04-13 03:03:31, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 44
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002704645 0.0002740287 0.0003061065 0.0041563208 0.0120425968 0.0259547286
Max. Mean SD
0.3993959563 0.0433199485 0.0939557543
Accuracy with training data: 98.5%
Out-of-bag accuracy: 90.1%
Gene: A
Training dataset: 34 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 47
avg. # of SNPs in an individual classifier: 15.00
(sd: 2.94, min: 12, max: 19, median: 14.50)
avg. # of haplotypes in an individual classifier: 36.25
(sd: 10.47, min: 21, max: 44, median: 40.00)
avg. out-of-bag accuracy: 90.12%
(sd: 2.59%, min: 86.36%, max: 92.31%, median: 90.91%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002704645 0.0002740287 0.0003061065 0.0041563208 0.0120425968 0.0259547286
Max. Mean SD
0.3993959563 0.0433199485 0.0939557543
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:31) 0%
Predicting (2019-04-13 03:03:31) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 1 (3.8%) 8 (30.8%) 16 (61.5%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0009577 0.0048947 0.0311502 0.0380943 0.3993960
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:31) 0%
Predicting (2019-04-13 03:03:31) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 1 (3.8%) 8 (30.8%) 16 (61.5%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0009577 0.0048947 0.0311502 0.0380943 0.3993960
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
SNP genotypes:
90 samples X 3932 SNPs
SNPs range from 28694391bp to 33426848bp on hg19
Missing rate per SNP:
min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489
Missing rate per sample:
min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554
Minor allele frequency:
min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144
Allelic information:
A/G C/T G/T A/C C/G A/T
1567 1510 348 332 111 64
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 5316 SNPs from chromosome 6.
SNP genotypes:
90 samples X 5316 SNPs
SNPs range from 25651262bp to 33426848bp on hg19
Missing rate per SNP:
min: 0, max: 0.1, mean: 0.0882054, median: 0.1, sd: 0.030674
Missing rate per sample:
min: 0, max: 0.863619, mean: 0.0882054, median: 0.00131678, sd: 0.259735
Minor allele frequency:
min: 0, max: 0.5, mean: 0.201867, median: 0.179012, sd: 0.155475
Allelic information:
A/G C/T G/T A/C C/G A/T
2102 2046 480 471 134 83
Exclude 1 monomorphic SNP
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 9
# of SNPs: 77, # of samples: 60
# of unique HLA alleles: 12
[-] 2019-04-13 03:03:31
[1] 2019-04-13 03:03:31, OOB Acc: 98.00%, # of SNPs: 13, # of Haplo: 20
[2] 2019-04-13 03:03:31, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 21
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02
Max. Mean SD
4.728168e-01 4.409445e-02 1.068815e-01
Accuracy with training data: 95.0%
Out-of-bag accuracy: 94.5%
Gene: DQB1
Training dataset: 60 samples X 77 SNPs
# of HLA alleles: 12
# of individual classifiers: 2
total # of SNPs used: 20
avg. # of SNPs in an individual classifier: 14.00
(sd: 1.41, min: 13, max: 15, median: 14.00)
avg. # of haplotypes in an individual classifier: 20.50
(sd: 0.71, min: 20, max: 21, median: 20.50)
avg. out-of-bag accuracy: 94.45%
(sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02
Max. Mean SD
4.728168e-01 4.409445e-02 1.068815e-01
Genome assembly: hg19
The HIBAG model:
There are 77 SNP predictors in total.
There are 2 individual classifiers.
Summarize the missing fractions of SNP predictors per classifier:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 60
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 100
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 100
# of unique HLA alleles: 0
# of unique HLA genotypes: 0
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 200
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Exclude 9 monomorphic SNPs
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:31
[1] 2019-04-13 03:03:31, OOB Acc: 86.96%, # of SNPs: 11, # of Haplo: 48
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.898504e-09 2.973325e-09 3.646720e-09 1.495845e-03 1.516334e-02 6.687055e-02
Max. Mean SD
9.834029e-01 9.608437e-02 2.420052e-01
Accuracy with training data: 92.5%
Out-of-bag accuracy: 87.0%
Exclude 9 monomorphic SNPs
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:31
[1] 2019-04-13 03:03:31, OOB Acc: 82.61%, # of SNPs: 14, # of Haplo: 54
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
6.946528e-15 6.575829e-14 5.950641e-13 1.660680e-03 6.378961e-03 1.518505e-02
Max. Mean SD
6.400032e-01 4.484927e-02 1.420419e-01
Accuracy with training data: 92.5%
Out-of-bag accuracy: 82.6%
Gene: A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 23
avg. # of SNPs in an individual classifier: 12.50
(sd: 2.12, min: 11, max: 14, median: 12.50)
avg. # of haplotypes in an individual classifier: 51.00
(sd: 4.24, min: 48, max: 54, median: 51.00)
avg. out-of-bag accuracy: 84.78%
(sd: 3.07%, min: 82.61%, max: 86.96%, median: 84.78%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.006377e-08 1.077445e-08 1.717056e-08 3.680759e-03 1.272457e-02 4.469180e-02
Max. Mean SD
8.117031e-01 7.046682e-02 1.896453e-01
Genome assembly: hg19
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 24
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:32
1, SNP: 155, Loss: 186.198, OOB Acc: 68.18%, # of Haplo: 13
2, SNP: 111, Loss: 138.902, OOB Acc: 72.73%, # of Haplo: 13
3, SNP: 159, Loss: 134.624, OOB Acc: 81.82%, # of Haplo: 15
4, SNP: 75, Loss: 120.53, OOB Acc: 86.36%, # of Haplo: 16
5, SNP: 129, Loss: 79.3436, OOB Acc: 90.91%, # of Haplo: 20
6, SNP: 135, Loss: 56.5856, OOB Acc: 90.91%, # of Haplo: 20
7, SNP: 162, Loss: 40.1347, OOB Acc: 90.91%, # of Haplo: 20
8, SNP: 192, Loss: 29.7565, OOB Acc: 90.91%, # of Haplo: 24
9, SNP: 182, Loss: 23.1586, OOB Acc: 90.91%, # of Haplo: 24
10, SNP: 24, Loss: 16.7007, OOB Acc: 90.91%, # of Haplo: 27
11, SNP: 154, Loss: 15.6368, OOB Acc: 90.91%, # of Haplo: 28
12, SNP: 17, Loss: 15.4059, OOB Acc: 90.91%, # of Haplo: 39
13, SNP: 199, Loss: 14.4894, OOB Acc: 90.91%, # of Haplo: 40
14, SNP: 73, Loss: 8.03154, OOB Acc: 90.91%, # of Haplo: 42
15, SNP: 151, Loss: 5.75619, OOB Acc: 90.91%, # of Haplo: 42
[1] 2019-04-13 03:03:32, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 42
1, SNP: 87, Loss: 163.788, OOB Acc: 50.00%, # of Haplo: 15
2, SNP: 179, Loss: 159.036, OOB Acc: 57.69%, # of Haplo: 16
3, SNP: 162, Loss: 127.744, OOB Acc: 61.54%, # of Haplo: 17
4, SNP: 113, Loss: 120.348, OOB Acc: 76.92%, # of Haplo: 17
5, SNP: 149, Loss: 99.7027, OOB Acc: 80.77%, # of Haplo: 19
6, SNP: 183, Loss: 63.9201, OOB Acc: 80.77%, # of Haplo: 19
7, SNP: 82, Loss: 46.9454, OOB Acc: 80.77%, # of Haplo: 19
8, SNP: 73, Loss: 36.2707, OOB Acc: 84.62%, # of Haplo: 21
9, SNP: 47, Loss: 34.9876, OOB Acc: 84.62%, # of Haplo: 22
10, SNP: 140, Loss: 27.9494, OOB Acc: 84.62%, # of Haplo: 22
11, SNP: 189, Loss: 23.9141, OOB Acc: 84.62%, # of Haplo: 23
12, SNP: 249, Loss: 18.884, OOB Acc: 84.62%, # of Haplo: 27
13, SNP: 151, Loss: 15.8511, OOB Acc: 84.62%, # of Haplo: 27
14, SNP: 21, Loss: 11.1982, OOB Acc: 88.46%, # of Haplo: 30
15, SNP: 215, Loss: 10.7762, OOB Acc: 88.46%, # of Haplo: 32
16, SNP: 136, Loss: 10.5472, OOB Acc: 88.46%, # of Haplo: 38
17, SNP: 117, Loss: 10.3045, OOB Acc: 88.46%, # of Haplo: 38
18, SNP: 106, Loss: 10.3041, OOB Acc: 92.31%, # of Haplo: 38
19, SNP: 199, Loss: 9.5325, OOB Acc: 92.31%, # of Haplo: 38
[2] 2019-04-13 03:03:32, OOB Acc: 92.31%, # of SNPs: 19, # of Haplo: 38
1, SNP: 135, Loss: 156.001, OOB Acc: 50.00%, # of Haplo: 13
2, SNP: 141, Loss: 121.763, OOB Acc: 59.09%, # of Haplo: 14
3, SNP: 162, Loss: 110.134, OOB Acc: 63.64%, # of Haplo: 14
4, SNP: 73, Loss: 95.625, OOB Acc: 72.73%, # of Haplo: 15
5, SNP: 96, Loss: 87.0485, OOB Acc: 86.36%, # of Haplo: 16
6, SNP: 129, Loss: 49.5862, OOB Acc: 86.36%, # of Haplo: 18
7, SNP: 147, Loss: 27.5253, OOB Acc: 86.36%, # of Haplo: 19
8, SNP: 105, Loss: 16.9538, OOB Acc: 86.36%, # of Haplo: 19
9, SNP: 258, Loss: 11.9891, OOB Acc: 86.36%, # of Haplo: 19
10, SNP: 160, Loss: 10.5954, OOB Acc: 86.36%, # of Haplo: 20
11, SNP: 199, Loss: 9.81573, OOB Acc: 86.36%, # of Haplo: 20
12, SNP: 151, Loss: 9.3333, OOB Acc: 86.36%, # of Haplo: 21
[3] 2019-04-13 03:03:32, OOB Acc: 86.36%, # of SNPs: 12, # of Haplo: 21
1, SNP: 184, Loss: 221.947, OOB Acc: 63.64%, # of Haplo: 15
2, SNP: 87, Loss: 159.682, OOB Acc: 77.27%, # of Haplo: 18
3, SNP: 84, Loss: 135.191, OOB Acc: 81.82%, # of Haplo: 21
4, SNP: 36, Loss: 128.841, OOB Acc: 86.36%, # of Haplo: 23
5, SNP: 109, Loss: 94.3071, OOB Acc: 90.91%, # of Haplo: 27
6, SNP: 130, Loss: 66.1551, OOB Acc: 90.91%, # of Haplo: 29
7, SNP: 64, Loss: 50.915, OOB Acc: 90.91%, # of Haplo: 29
8, SNP: 194, Loss: 41.1566, OOB Acc: 90.91%, # of Haplo: 29
9, SNP: 193, Loss: 32.6689, OOB Acc: 90.91%, # of Haplo: 30
10, SNP: 123, Loss: 30.6255, OOB Acc: 90.91%, # of Haplo: 30
11, SNP: 55, Loss: 30.1894, OOB Acc: 90.91%, # of Haplo: 35
12, SNP: 95, Loss: 29.8967, OOB Acc: 90.91%, # of Haplo: 43
13, SNP: 151, Loss: 25.6379, OOB Acc: 90.91%, # of Haplo: 43
14, SNP: 199, Loss: 24.4761, OOB Acc: 90.91%, # of Haplo: 44
[4] 2019-04-13 03:03:32, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 44
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002704645 0.0002740287 0.0003061065 0.0041563208 0.0120425968 0.0259547286
Max. Mean SD
0.3993959563 0.0433199485 0.0939557543
Accuracy with training data: 98.5%
Out-of-bag accuracy: 90.1%
Gene: A
Training dataset: 34 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 47
avg. # of SNPs in an individual classifier: 15.00
(sd: 2.94, min: 12, max: 19, median: 14.50)
avg. # of haplotypes in an individual classifier: 36.25
(sd: 10.47, min: 21, max: 44, median: 40.00)
avg. out-of-bag accuracy: 90.12%
(sd: 2.59%, min: 86.36%, max: 92.31%, median: 90.91%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002704645 0.0002740287 0.0003061065 0.0041563208 0.0120425968 0.0259547286
Max. Mean SD
0.3993959563 0.0433199485 0.0939557543
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:32) 0%
Predicting (2019-04-13 03:03:32) 100%
Allelic ambiguity: 01:01, 02:02
Allelic ambiguity: 01:01, 02:02
Allelic ambiguity: 09:01
Allelic ambiguity: 09:01
Allelic ambiguity: 05:01, 06:01
Allelic ambiguity: 05:01, 06:01
Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01
Pos Num * - A D E F G H I K L M N Q R S T V W Y
1 120 120 . . . . . . . . . . . . . . . . . . .
9 120 . 81 . . . . . . . . . . . . . 15 7 . . 17
44 120 . 25 . . . . . . . . . . . . 95 . . . . .
56 120 . 117 . . . . . . . . . . . . 3 . . . . .
62 120 . 46 . . 15 . 44 . . . 4 . . . 11 . . . . .
63 120 . 105 . . . . . . . . . . 11 4 . . . . . .
65 120 . 105 . . . . 15 . . . . . . . . . . . . .
66 120 . 61 . . . . . . . 59 . . . . . . . . . .
67 120 . 25 . . . . . . . . . . . . . . . 95 . .
70 120 . 99 . . . . . . . . . . . 21 . . . . . .
73 120 . 117 . . . . . . 3 . . . . . . . . . . .
74 120 . 76 . . . . . 44 . . . . . . . . . . . .
76 120 . 32 . . 24 . . . . . . . . . . . . 64 . .
77 120 . 47 . 64 . . . . . . . . . . . 9 . . . .
79 120 . 96 . . . . . . . . . . . . 24 . . . . .
80 120 . 96 . . . . . . 24 . . . . . . . . . . .
81 120 . 96 24 . . . . . . . . . . . . . . . . .
82 120 . 96 . . . . . . . . 24 . . . . . . . . .
83 120 . 96 . . . . . . . . . . . . 24 . . . . .
90 120 . 38 82 . . . . . . . . . . . . . . . . .
95 120 . 61 . . . . . . . . 15 . . . . . . 44 . .
97 120 . 39 . . . . . . . . . 29 . . 52 . . . . .
99 120 . 105 . . . 15 . . . . . . . . . . . . . .
105 120 . 42 . . . . . . . . . . . . . 78 . . . .
107 120 . 76 . . . . . . . . . . . . . . . . 44 .
109 120 . 116 . . . . . . . . 4 . . . . . . . . .
114 120 . 46 . . . . . 59 . . . . . 15 . . . . . .
116 120 . 61 . . . . . . . . . . . . . . . . . 59
127 120 . 58 . . . . . . . 62 . . . . . . . . . .
142 120 . 73 . . . . . . . . . . . . . . 47 . . .
144 120 . 98 . . . . . . . . . . . 22 . . . . . .
145 120 . 73 . . . . . 47 . . . . . . . . . . . .
149 120 . 112 . . . . . . . . . . . . . . 8 . . .
150 120 . 25 95 . . . . . . . . . . . . . . . . .
151 120 . 106 . . . . . . . . . . . . 14 . . . . .
152 120 . 30 . . 17 . . . . . . . . . . . . 73 . .
156 120 . 25 . . . . . . . . 67 . . 17 . . . . 11 .
158 120 . 25 95 . . . . . . . . . . . . . . . . .
161 120 . 111 . 9 . . . . . . . . . . . . . . . .
163 120 . 38 . . . . . . . . . . . . . . 82 . . .
166 120 . 39 . . 81 . . . . . . . . . . . . . . .
167 120 . 39 . . . . . . . . . . . . . . . . 81 .
183 120 120 . . . . . . . . . . . . . . . . . . .
Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01
Pos Num * - A D E F G H I K L M N Q R S T V W Y
-23 120 120 . . . . . . . . . . . . . . . . . . .
-22 120 120 . . . . . . . . . . . . . . . . . . .
-21 120 120 . . . . . . . . . . . . . . . . . . .
-20 120 120 . . . . . . . . . . . . . . . . . . .
-19 120 120 . . . . . . . . . . . . . . . . . . .
-18 120 120 . . . . . . . . . . . . . . . . . . .
-17 120 120 . . . . . . . . . . . . . . . . . . .
-16 120 120 . . . . . . . . . . . . . . . . . . .
-15 120 120 . . . . . . . . . . . . . . . . . . .
-14 120 120 . . . . . . . . . . . . . . . . . . .
-13 120 120 . . . . . . . . . . . . . . . . . . .
-12 120 120 . . . . . . . . . . . . . . . . . . .
-11 120 120 . . . . . . . . . . . . . . . . . . .
-10 120 120 . . . . . . . . . . . . . . . . . . .
-9 120 120 . . . . . . . . . . . . . . . . . . .
-8 120 120 . . . . . . . . . . . . . . . . . . .
-7 120 120 . . . . . . . . . . . . . . . . . . .
-6 120 120 . . . . . . . . . . . . . . . . . . .
-5 120 120 . . . . . . . . . . . . . . . . . . .
-4 120 120 . . . . . . . . . . . . . . . . . . .
-3 120 120 . . . . . . . . . . . . . . . . . . .
-2 120 120 . . . . . . . . . . . . . . . . . . .
-1 120 120 . . . . . . . . . . . . . . . . . . .
. 120 120 . . . . . . . . . . . . . . . . . . .
1 120 120 . . . . . . . . . . . . . . . . . . .
9 120 . 81 . . . . . . . . . . . . . 15 7 . . 17
44 120 . 25 . . . . . . . . . . . . 95 . . . . .
56 120 . 117 . . . . . . . . . . . . 3 . . . . .
62 120 . 46 . . 15 . 44 . . . 4 . . . 11 . . . . .
63 120 . 105 . . . . . . . . . . 11 4 . . . . . .
65 120 . 105 . . . . 15 . . . . . . . . . . . . .
66 120 . 61 . . . . . . . 59 . . . . . . . . . .
67 120 . 25 . . . . . . . . . . . . . . . 95 . .
70 120 . 99 . . . . . . . . . . . 21 . . . . . .
73 120 . 117 . . . . . . 3 . . . . . . . . . . .
74 120 . 76 . . . . . 44 . . . . . . . . . . . .
76 120 . 32 . . 24 . . . . . . . . . . . . 64 . .
77 120 . 47 . 64 . . . . . . . . . . . 9 . . . .
79 120 . 96 . . . . . . . . . . . . 24 . . . . .
80 120 . 96 . . . . . . 24 . . . . . . . . . . .
81 120 . 96 24 . . . . . . . . . . . . . . . . .
82 120 . 96 . . . . . . . . 24 . . . . . . . . .
83 120 . 96 . . . . . . . . . . . . 24 . . . . .
90 120 . 38 82 . . . . . . . . . . . . . . . . .
95 120 . 61 . . . . . . . . 15 . . . . . . 44 . .
97 120 . 39 . . . . . . . . . 29 . . 52 . . . . .
99 120 . 105 . . . 15 . . . . . . . . . . . . . .
105 120 . 42 . . . . . . . . . . . . . 78 . . . .
107 120 . 76 . . . . . . . . . . . . . . . . 44 .
109 120 . 116 . . . . . . . . 4 . . . . . . . . .
114 120 . 46 . . . . . 59 . . . . . 15 . . . . . .
116 120 . 61 . . . . . . . . . . . . . . . . . 59
127 120 . 58 . . . . . . . 62 . . . . . . . . . .
142 120 . 73 . . . . . . . . . . . . . . 47 . . .
144 120 . 98 . . . . . . . . . . . 22 . . . . . .
145 120 . 73 . . . . . 47 . . . . . . . . . . . .
149 120 . 112 . . . . . . . . . . . . . . 8 . . .
150 120 . 25 95 . . . . . . . . . . . . . . . . .
151 120 . 106 . . . . . . . . . . . . 14 . . . . .
152 120 . 30 . . 17 . . . . . . . . . . . . 73 . .
156 120 . 25 . . . . . . . . 67 . . 17 . . . . 11 .
158 120 . 25 95 . . . . . . . . . . . . . . . . .
161 120 . 111 . 9 . . . . . . . . . . . . . . . .
163 120 . 38 . . . . . . . . . . . . . . 82 . . .
166 120 . 39 . . 81 . . . . . . . . . . . . . . .
167 120 . 39 . . . . . . . . . . . . . . . . 81 .
183 120 120 . . . . . . . . . . . . . . . . . . .
184 120 120 . . . . . . . . . . . . . . . . . . .
185 120 120 . . . . . . . . . . . . . . . . . . .
186 120 120 . . . . . . . . . . . . . . . . . . .
187 120 120 . . . . . . . . . . . . . . . . . . .
188 120 120 . . . . . . . . . . . . . . . . . . .
189 120 120 . . . . . . . . . . . . . . . . . . .
190 120 120 . . . . . . . . . . . . . . . . . . .
191 120 120 . . . . . . . . . . . . . . . . . . .
192 120 120 . . . . . . . . . . . . . . . . . . .
193 120 120 . . . . . . . . . . . . . . . . . . .
194 120 120 . . . . . . . . . . . . . . . . . . .
195 120 120 . . . . . . . . . . . . . . . . . . .
196 120 120 . . . . . . . . . . . . . . . . . . .
197 120 120 . . . . . . . . . . . . . . . . . . .
198 120 120 . . . . . . . . . . . . . . . . . . .
199 120 120 . . . . . . . . . . . . . . . . . . .
200 120 120 . . . . . . . . . . . . . . . . . . .
201 120 120 . . . . . . . . . . . . . . . . . . .
202 120 120 . . . . . . . . . . . . . . . . . . .
203 120 120 . . . . . . . . . . . . . . . . . . .
204 120 120 . . . . . . . . . . . . . . . . . . .
205 120 120 . . . . . . . . . . . . . . . . . . .
206 120 120 . . . . . . . . . . . . . . . . . . .
207 120 120 . . . . . . . . . . . . . . . . . . .
208 120 120 . . . . . . . . . . . . . . . . . . .
209 120 120 . . . . . . . . . . . . . . . . . . .
210 120 120 . . . . . . . . . . . . . . . . . . .
211 120 120 . . . . . . . . . . . . . . . . . . .
212 120 120 . . . . . . . . . . . . . . . . . . .
213 120 120 . . . . . . . . . . . . . . . . . . .
214 120 120 . . . . . . . . . . . . . . . . . . .
215 120 120 . . . . . . . . . . . . . . . . . . .
216 120 120 . . . . . . . . . . . . . . . . . . .
217 120 120 . . . . . . . . . . . . . . . . . . .
218 120 120 . . . . . . . . . . . . . . . . . . .
219 120 120 . . . . . . . . . . . . . . . . . . .
220 120 120 . . . . . . . . . . . . . . . . . . .
221 120 120 . . . . . . . . . . . . . . . . . . .
222 120 120 . . . . . . . . . . . . . . . . . . .
223 120 120 . . . . . . . . . . . . . . . . . . .
224 120 120 . . . . . . . . . . . . . . . . . . .
225 120 120 . . . . . . . . . . . . . . . . . . .
226 120 120 . . . . . . . . . . . . . . . . . . .
227 120 120 . . . . . . . . . . . . . . . . . . .
228 120 120 . . . . . . . . . . . . . . . . . . .
229 120 120 . . . . . . . . . . . . . . . . . . .
230 120 120 . . . . . . . . . . . . . . . . . . .
231 120 120 . . . . . . . . . . . . . . . . . . .
232 120 120 . . . . . . . . . . . . . . . . . . .
233 120 120 . . . . . . . . . . . . . . . . . . .
234 120 120 . . . . . . . . . . . . . . . . . . .
235 120 120 . . . . . . . . . . . . . . . . . . .
236 120 120 . . . . . . . . . . . . . . . . . . .
237 120 120 . . . . . . . . . . . . . . . . . . .
238 120 120 . . . . . . . . . . . . . . . . . . .
239 120 120 . . . . . . . . . . . . . . . . . . .
240 120 120 . . . . . . . . . . . . . . . . . . .
241 120 120 . . . . . . . . . . . . . . . . . . .
242 120 120 . . . . . . . . . . . . . . . . . . .
243 120 120 . . . . . . . . . . . . . . . . . . .
244 120 120 . . . . . . . . . . . . . . . . . . .
245 120 120 . . . . . . . . . . . . . . . . . . .
246 120 120 . . . . . . . . . . . . . . . . . . .
247 120 120 . . . . . . . . . . . . . . . . . . .
248 120 120 . . . . . . . . . . . . . . . . . . .
249 120 120 . . . . . . . . . . . . . . . . . . .
250 120 120 . . . . . . . . . . . . . . . . . . .
251 120 120 . . . . . . . . . . . . . . . . . . .
252 120 120 . . . . . . . . . . . . . . . . . . .
253 120 120 . . . . . . . . . . . . . . . . . . .
254 120 120 . . . . . . . . . . . . . . . . . . .
255 120 120 . . . . . . . . . . . . . . . . . . .
256 120 120 . . . . . . . . . . . . . . . . . . .
257 120 120 . . . . . . . . . . . . . . . . . . .
258 120 120 . . . . . . . . . . . . . . . . . . .
259 120 120 . . . . . . . . . . . . . . . . . . .
260 120 120 . . . . . . . . . . . . . . . . . . .
261 120 120 . . . . . . . . . . . . . . . . . . .
262 120 120 . . . . . . . . . . . . . . . . . . .
263 120 120 . . . . . . . . . . . . . . . . . . .
264 120 120 . . . . . . . . . . . . . . . . . . .
265 120 120 . . . . . . . . . . . . . . . . . . .
266 120 120 . . . . . . . . . . . . . . . . . . .
267 120 120 . . . . . . . . . . . . . . . . . . .
268 120 120 . . . . . . . . . . . . . . . . . . .
269 120 120 . . . . . . . . . . . . . . . . . . .
270 120 120 . . . . . . . . . . . . . . . . . . .
271 120 120 . . . . . . . . . . . . . . . . . . .
272 120 120 . . . . . . . . . . . . . . . . . . .
273 120 120 . . . . . . . . . . . . . . . . . . .
274 120 120 . . . . . . . . . . . . . . . . . . .
275 120 120 . . . . . . . . . . . . . . . . . . .
276 120 120 . . . . . . . . . . . . . . . . . . .
277 120 120 . . . . . . . . . . . . . . . . . . .
278 120 120 . . . . . . . . . . . . . . . . . . .
279 120 120 . . . . . . . . . . . . . . . . . . .
280 120 120 . . . . . . . . . . . . . . . . . . .
281 120 120 . . . . . . . . . . . . . . . . . . .
282 120 120 . . . . . . . . . . . . . . . . . . .
283 120 120 . . . . . . . . . . . . . . . . . . .
284 120 120 . . . . . . . . . . . . . . . . . . .
285 120 120 . . . . . . . . . . . . . . . . . . .
286 120 120 . . . . . . . . . . . . . . . . . . .
287 120 120 . . . . . . . . . . . . . . . . . . .
288 120 120 . . . . . . . . . . . . . . . . . . .
289 120 120 . . . . . . . . . . . . . . . . . . .
290 120 120 . . . . . . . . . . . . . . . . . . .
291 120 120 . . . . . . . . . . . . . . . . . . .
292 120 120 . . . . . . . . . . . . . . . . . . .
293 120 120 . . . . . . . . . . . . . . . . . . .
294 120 120 . . . . . . . . . . . . . . . . . . .
295 120 120 . . . . . . . . . . . . . . . . . . .
296 120 120 . . . . . . . . . . . . . . . . . . .
297 120 120 . . . . . . . . . . . . . . . . . . .
298 120 120 . . . . . . . . . . . . . . . . . . .
299 120 120 . . . . . . . . . . . . . . . . . . .
300 120 120 . . . . . . . . . . . . . . . . . . .
301 120 120 . . . . . . . . . . . . . . . . . . .
302 120 120 . . . . . . . . . . . . . . . . . . .
303 120 120 . . . . . . . . . . . . . . . . . . .
304 120 120 . . . . . . . . . . . . . . . . . . .
305 120 120 . . . . . . . . . . . . . . . . . . .
306 120 120 . . . . . . . . . . . . . . . . . . .
307 120 120 . . . . . . . . . . . . . . . . . . .
308 120 120 . . . . . . . . . . . . . . . . . . .
309 120 120 . . . . . . . . . . . . . . . . . . .
310 120 120 . . . . . . . . . . . . . . . . . . .
311 120 120 . . . . . . . . . . . . . . . . . . .
312 120 120 . . . . . . . . . . . . . . . . . . .
313 120 120 . . . . . . . . . . . . . . . . . . .
314 120 120 . . . . . . . . . . . . . . . . . . .
315 120 120 . . . . . . . . . . . . . . . . . . .
316 120 120 . . . . . . . . . . . . . . . . . . .
317 120 120 . . . . . . . . . . . . . . . . . . .
318 120 120 . . . . . . . . . . . . . . . . . . .
319 120 120 . . . . . . . . . . . . . . . . . . .
320 120 120 . . . . . . . . . . . . . . . . . . .
321 120 120 . . . . . . . . . . . . . . . . . . .
322 120 120 . . . . . . . . . . . . . . . . . . .
323 120 120 . . . . . . . . . . . . . . . . . . .
324 120 120 . . . . . . . . . . . . . . . . . . .
325 120 120 . . . . . . . . . . . . . . . . . . .
326 120 120 . . . . . . . . . . . . . . . . . . .
327 120 120 . . . . . . . . . . . . . . . . . . .
328 120 120 . . . . . . . . . . . . . . . . . . .
329 120 120 . . . . . . . . . . . . . . . . . . .
330 120 120 . . . . . . . . . . . . . . . . . . .
331 120 120 . . . . . . . . . . . . . . . . . . .
332 120 120 . . . . . . . . . . . . . . . . . . .
333 120 120 . . . . . . . . . . . . . . . . . . .
334 120 120 . . . . . . . . . . . . . . . . . . .
335 120 120 . . . . . . . . . . . . . . . . . . .
336 120 120 . . . . . . . . . . . . . . . . . . .
337 120 120 . . . . . . . . . . . . . . . . . . .
338 120 120 . . . . . . . . . . . . . . . . . . .
339 120 120 . . . . . . . . . . . . . . . . . . .
340 120 120 . . . . . . . . . . . . . . . . . . .
341 120 120 . . . . . . . . . . . . . . . . . . .
Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02
Pos Num * - A D E F G I K L M N P Q R S T Y
5 120 112 . . . . . . . . . . 8 . . . . . .
6 120 20 92 8 . . . . . . . . . . . . . . .
7 112 20 92 . . . . . . . . . . . . . . . .
8 112 20 92 . . . . . . . . . . . . . . . .
9 112 3 76 . . . 33 . . . . . . . . . . . .
10 112 3 109 . . . . . . . . . . . . . . . .
11 112 3 109 . . . . . . . . . . . . . . . .
12 112 3 109 . . . . . . . . . . . . . . . .
13 112 3 93 16 . . . . . . . . . . . . . . .
14 112 3 14 . . . . . . . . 95 . . . . . . .
15 112 3 109 . . . . . . . . . . . . . . . .
16 112 3 109 . . . . . . . . . . . . . . . .
17 112 3 109 . . . . . . . . . . . . . . . .
18 112 3 109 . . . . . . . . . . . . . . . .
19 112 3 109 . . . . . . . . . . . . . . . .
20 112 3 109 . . . . . . . . . . . . . . . .
26 112 . 20 . . . . . . . 76 . . . . . . . 16
28 112 . 100 . . . . . . . . . . . . . 12 . .
30 112 . 24 . . . . . . . . . . . . . 12 . 76
37 112 . 100 . . . . . 12 . . . . . . . . . .
38 112 . 29 83 . . . . . . . . . . . . . . .
45 112 . 96 . . 16 . . . . . . . . . . . . .
46 112 . 100 . . 12 . . . . . . . . . . . . .
47 112 . 100 . . . 12 . . . . . . . . . . . .
52 112 . 100 . . . . . . . 12 . . . . . . . .
53 112 . 54 . . . . . . . 58 . . . . . . . .
55 112 . 57 . . . . . . . 12 . . 43 . . . . .
56 112 . 109 . . . . . . . 3 . . . . . . . .
57 112 . 14 33 64 . . . . . . . . . . . 1 . .
66 112 . 97 . 15 . . . . . . . . . . . . . .
67 112 . 97 . . . . . 15 . . . . . . . . . .
70 112 3 50 . . 3 . . . . . . . . . 56 . . .
71 112 3 14 . 3 . . . . 12 . . . . . . . 80 .
72 112 3 109 . . . . . . . . . . . . . . . .
73 112 3 109 . . . . . . . . . . . . . . . .
74 112 3 17 12 . 80 . . . . . . . . . . . . .
75 112 3 29 . . . . . . . 80 . . . . . . . .
76 112 3 109 . . . . . . . . . . . . . . . .
77 112 3 26 . . . . . . . . . . . . . . 83 .
78 112 3 109 . . . . . . . . . . . . . . . .
79 112 3 109 . . . . . . . . . . . . . . . .
80 112 3 109 . . . . . . . . . . . . . . . .
81 112 3 109 . . . . . . . . . . . . . . . .
82 112 3 109 . . . . . . . . . . . . . . . .
83 112 3 109 . . . . . . . . . . . . . . . .
84 112 3 51 . . . . . . . . . . . 58 . . . .
85 112 3 51 . . . . . . . 58 . . . . . . . .
86 112 3 50 . . 58 . 1 . . . . . . . . . . .
87 112 3 15 . . . 36 . . . 58 . . . . . . . .
88 112 3 109 . . . . . . . . . . . . . . . .
89 112 3 51 . . . . . . . . . . . . . . 58 .
90 112 3 51 . . . . . . . . . . . . . . 58 .
91 112 3 109 . . . . . . . . . . . . . . . .
92 112 3 109 . . . . . . . . . . . . . . . .
93 112 3 109 . . . . . . . . . . . . . . . .
94 112 17 95 . . . . . . . . . . . . . . . .
95 112 112 . . . . . . . . . . . . . . . . .
Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02
Pos Num * - A D E F G I K L M N P Q R S T Y
-31 120 112 . . . . . . . . . . 8 . . . . . .
-30 120 112 . 8 . . . . . . . . . . . . . . .
-29 112 112 . . . . . . . . . . . . . . . . .
-28 112 112 . . . . . . . . . . . . . . . . .
-27 112 112 . . . . . . . . . . . . . . . . .
-26 112 112 . . . . . . . . . . . . . . . . .
-25 112 112 . . . . . . . . . . . . . . . . .
-24 112 112 . . . . . . . . . . . . . . . . .
-23 112 112 . . . . . . . . . . . . . . . . .
-22 112 112 . . . . . . . . . . . . . . . . .
-21 112 112 . . . . . . . . . . . . . . . . .
-20 112 112 . . . . . . . . . . . . . . . . .
-19 112 112 . . . . . . . . . . . . . . . . .
-18 112 112 . . . . . . . . . . . . . . . . .
-17 112 112 . . . . . . . . . . . . . . . . .
-16 112 112 . . . . . . . . . . . . . . . . .
-15 112 112 . . . . . . . . . . . . . . . . .
-14 112 112 . . . . . . . . . . . . . . . . .
-13 112 112 . . . . . . . . . . . . . . . . .
-12 112 112 . . . . . . . . . . . . . . . . .
-11 112 112 . . . . . . . . . . . . . . . . .
-10 112 112 . . . . . . . . . . . . . . . . .
-9 112 112 . . . . . . . . . . . . . . . . .
-8 112 112 . . . . . . . . . . . . . . . . .
-7 112 112 . . . . . . . . . . . . . . . . .
-6 112 112 . . . . . . . . . . . . . . . . .
-5 112 112 . . . . . . . . . . . . . . . . .
-4 112 112 . . . . . . . . . . . . . . . . .
-3 112 112 . . . . . . . . . . . . . . . . .
-2 112 112 . . . . . . . . . . . . . . . . .
-1 112 112 . . . . . . . . . . . . . . . . .
. 112 112 . . . . . . . . . . . . . . . . .
1 112 112 . . . . . . . . . . . . . . . . .
2 112 112 . . . . . . . . . . . . . . . . .
3 112 112 . . . . . . . . . . . . . . . . .
4 112 112 . . . . . . . . . . . . . . . . .
5 112 112 . . . . . . . . . . . . . . . . .
6 112 20 92 . . . . . . . . . . . . . . . .
7 112 20 92 . . . . . . . . . . . . . . . .
8 112 20 92 . . . . . . . . . . . . . . . .
9 112 3 76 . . . 33 . . . . . . . . . . . .
10 112 3 109 . . . . . . . . . . . . . . . .
11 112 3 109 . . . . . . . . . . . . . . . .
12 112 3 109 . . . . . . . . . . . . . . . .
13 112 3 93 16 . . . . . . . . . . . . . . .
14 112 3 14 . . . . . . . . 95 . . . . . . .
15 112 3 109 . . . . . . . . . . . . . . . .
16 112 3 109 . . . . . . . . . . . . . . . .
17 112 3 109 . . . . . . . . . . . . . . . .
18 112 3 109 . . . . . . . . . . . . . . . .
19 112 3 109 . . . . . . . . . . . . . . . .
20 112 3 109 . . . . . . . . . . . . . . . .
26 112 . 20 . . . . . . . 76 . . . . . . . 16
28 112 . 100 . . . . . . . . . . . . . 12 . .
30 112 . 24 . . . . . . . . . . . . . 12 . 76
37 112 . 100 . . . . . 12 . . . . . . . . . .
38 112 . 29 83 . . . . . . . . . . . . . . .
45 112 . 96 . . 16 . . . . . . . . . . . . .
46 112 . 100 . . 12 . . . . . . . . . . . . .
47 112 . 100 . . . 12 . . . . . . . . . . . .
52 112 . 100 . . . . . . . 12 . . . . . . . .
53 112 . 54 . . . . . . . 58 . . . . . . . .
55 112 . 57 . . . . . . . 12 . . 43 . . . . .
56 112 . 109 . . . . . . . 3 . . . . . . . .
57 112 . 14 33 64 . . . . . . . . . . . 1 . .
66 112 . 97 . 15 . . . . . . . . . . . . . .
67 112 . 97 . . . . . 15 . . . . . . . . . .
70 112 3 50 . . 3 . . . . . . . . . 56 . . .
71 112 3 14 . 3 . . . . 12 . . . . . . . 80 .
72 112 3 109 . . . . . . . . . . . . . . . .
73 112 3 109 . . . . . . . . . . . . . . . .
74 112 3 17 12 . 80 . . . . . . . . . . . . .
75 112 3 29 . . . . . . . 80 . . . . . . . .
76 112 3 109 . . . . . . . . . . . . . . . .
77 112 3 26 . . . . . . . . . . . . . . 83 .
78 112 3 109 . . . . . . . . . . . . . . . .
79 112 3 109 . . . . . . . . . . . . . . . .
80 112 3 109 . . . . . . . . . . . . . . . .
81 112 3 109 . . . . . . . . . . . . . . . .
82 112 3 109 . . . . . . . . . . . . . . . .
83 112 3 109 . . . . . . . . . . . . . . . .
84 112 3 51 . . . . . . . . . . . 58 . . . .
85 112 3 51 . . . . . . . 58 . . . . . . . .
86 112 3 50 . . 58 . 1 . . . . . . . . . . .
87 112 3 15 . . . 36 . . . 58 . . . . . . . .
88 112 3 109 . . . . . . . . . . . . . . . .
89 112 3 51 . . . . . . . . . . . . . . 58 .
90 112 3 51 . . . . . . . . . . . . . . 58 .
91 112 3 109 . . . . . . . . . . . . . . . .
92 112 3 109 . . . . . . . . . . . . . . . .
93 112 3 109 . . . . . . . . . . . . . . . .
94 112 17 95 . . . . . . . . . . . . . . . .
95 112 112 . . . . . . . . . . . . . . . . .
96 112 112 . . . . . . . . . . . . . . . . .
97 112 112 . . . . . . . . . . . . . . . . .
98 112 112 . . . . . . . . . . . . . . . . .
99 112 112 . . . . . . . . . . . . . . . . .
100 112 112 . . . . . . . . . . . . . . . . .
101 112 112 . . . . . . . . . . . . . . . . .
102 112 112 . . . . . . . . . . . . . . . . .
103 112 112 . . . . . . . . . . . . . . . . .
104 112 112 . . . . . . . . . . . . . . . . .
105 112 112 . . . . . . . . . . . . . . . . .
106 112 112 . . . . . . . . . . . . . . . . .
107 112 112 . . . . . . . . . . . . . . . . .
108 112 112 . . . . . . . . . . . . . . . . .
109 112 112 . . . . . . . . . . . . . . . . .
110 112 112 . . . . . . . . . . . . . . . . .
111 112 112 . . . . . . . . . . . . . . . . .
112 112 112 . . . . . . . . . . . . . . . . .
113 112 112 . . . . . . . . . . . . . . . . .
114 112 112 . . . . . . . . . . . . . . . . .
115 112 112 . . . . . . . . . . . . . . . . .
116 112 112 . . . . . . . . . . . . . . . . .
117 112 112 . . . . . . . . . . . . . . . . .
118 112 112 . . . . . . . . . . . . . . . . .
119 112 112 . . . . . . . . . . . . . . . . .
120 112 112 . . . . . . . . . . . . . . . . .
121 112 112 . . . . . . . . . . . . . . . . .
122 112 112 . . . . . . . . . . . . . . . . .
123 112 112 . . . . . . . . . . . . . . . . .
124 112 112 . . . . . . . . . . . . . . . . .
125 112 112 . . . . . . . . . . . . . . . . .
126 112 112 . . . . . . . . . . . . . . . . .
127 112 112 . . . . . . . . . . . . . . . . .
128 112 112 . . . . . . . . . . . . . . . . .
129 112 112 . . . . . . . . . . . . . . . . .
130 112 112 . . . . . . . . . . . . . . . . .
131 112 112 . . . . . . . . . . . . . . . . .
132 112 112 . . . . . . . . . . . . . . . . .
133 112 112 . . . . . . . . . . . . . . . . .
134 112 112 . . . . . . . . . . . . . . . . .
135 112 112 . . . . . . . . . . . . . . . . .
136 112 112 . . . . . . . . . . . . . . . . .
137 112 112 . . . . . . . . . . . . . . . . .
138 112 112 . . . . . . . . . . . . . . . . .
139 112 112 . . . . . . . . . . . . . . . . .
140 112 112 . . . . . . . . . . . . . . . . .
141 112 112 . . . . . . . . . . . . . . . . .
142 112 112 . . . . . . . . . . . . . . . . .
143 112 112 . . . . . . . . . . . . . . . . .
144 112 112 . . . . . . . . . . . . . . . . .
145 112 112 . . . . . . . . . . . . . . . . .
146 112 112 . . . . . . . . . . . . . . . . .
147 112 112 . . . . . . . . . . . . . . . . .
148 112 112 . . . . . . . . . . . . . . . . .
149 112 112 . . . . . . . . . . . . . . . . .
150 112 112 . . . . . . . . . . . . . . . . .
151 112 112 . . . . . . . . . . . . . . . . .
152 112 112 . . . . . . . . . . . . . . . . .
153 112 112 . . . . . . . . . . . . . . . . .
154 112 112 . . . . . . . . . . . . . . . . .
155 112 112 . . . . . . . . . . . . . . . . .
156 112 112 . . . . . . . . . . . . . . . . .
157 112 112 . . . . . . . . . . . . . . . . .
158 112 112 . . . . . . . . . . . . . . . . .
159 112 112 . . . . . . . . . . . . . . . . .
160 112 112 . . . . . . . . . . . . . . . . .
161 112 112 . . . . . . . . . . . . . . . . .
162 112 112 . . . . . . . . . . . . . . . . .
163 112 112 . . . . . . . . . . . . . . . . .
164 112 112 . . . . . . . . . . . . . . . . .
165 112 112 . . . . . . . . . . . . . . . . .
166 112 112 . . . . . . . . . . . . . . . . .
167 112 112 . . . . . . . . . . . . . . . . .
168 112 112 . . . . . . . . . . . . . . . . .
169 112 112 . . . . . . . . . . . . . . . . .
170 112 112 . . . . . . . . . . . . . . . . .
171 112 112 . . . . . . . . . . . . . . . . .
172 112 112 . . . . . . . . . . . . . . . . .
173 112 112 . . . . . . . . . . . . . . . . .
174 112 112 . . . . . . . . . . . . . . . . .
175 112 112 . . . . . . . . . . . . . . . . .
176 112 112 . . . . . . . . . . . . . . . . .
177 112 112 . . . . . . . . . . . . . . . . .
178 112 112 . . . . . . . . . . . . . . . . .
179 112 112 . . . . . . . . . . . . . . . . .
180 112 112 . . . . . . . . . . . . . . . . .
181 112 112 . . . . . . . . . . . . . . . . .
182 112 112 . . . . . . . . . . . . . . . . .
183 112 112 . . . . . . . . . . . . . . . . .
184 112 112 . . . . . . . . . . . . . . . . .
185 112 112 . . . . . . . . . . . . . . . . .
186 112 112 . . . . . . . . . . . . . . . . .
187 112 112 . . . . . . . . . . . . . . . . .
188 112 112 . . . . . . . . . . . . . . . . .
189 112 112 . . . . . . . . . . . . . . . . .
190 112 112 . . . . . . . . . . . . . . . . .
191 112 112 . . . . . . . . . . . . . . . . .
192 112 112 . . . . . . . . . . . . . . . . .
193 112 112 . . . . . . . . . . . . . . . . .
194 112 112 . . . . . . . . . . . . . . . . .
195 112 112 . . . . . . . . . . . . . . . . .
196 112 112 . . . . . . . . . . . . . . . . .
197 112 112 . . . . . . . . . . . . . . . . .
198 112 112 . . . . . . . . . . . . . . . . .
199 112 112 . . . . . . . . . . . . . . . . .
200 112 112 . . . . . . . . . . . . . . . . .
201 112 112 . . . . . . . . . . . . . . . . .
202 112 112 . . . . . . . . . . . . . . . . .
203 112 112 . . . . . . . . . . . . . . . . .
204 112 112 . . . . . . . . . . . . . . . . .
205 112 112 . . . . . . . . . . . . . . . . .
206 112 112 . . . . . . . . . . . . . . . . .
207 112 112 . . . . . . . . . . . . . . . . .
208 112 112 . . . . . . . . . . . . . . . . .
209 112 112 . . . . . . . . . . . . . . . . .
210 112 112 . . . . . . . . . . . . . . . . .
211 112 112 . . . . . . . . . . . . . . . . .
212 112 112 . . . . . . . . . . . . . . . . .
213 112 112 . . . . . . . . . . . . . . . . .
214 112 112 . . . . . . . . . . . . . . . . .
215 112 112 . . . . . . . . . . . . . . . . .
216 112 112 . . . . . . . . . . . . . . . . .
217 112 112 . . . . . . . . . . . . . . . . .
218 112 112 . . . . . . . . . . . . . . . . .
219 112 112 . . . . . . . . . . . . . . . . .
220 112 112 . . . . . . . . . . . . . . . . .
221 112 112 . . . . . . . . . . . . . . . . .
222 112 112 . . . . . . . . . . . . . . . . .
223 112 112 . . . . . . . . . . . . . . . . .
224 112 112 . . . . . . . . . . . . . . . . .
225 112 112 . . . . . . . . . . . . . . . . .
226 112 112 . . . . . . . . . . . . . . . . .
227 112 112 . . . . . . . . . . . . . . . . .
228 112 112 . . . . . . . . . . . . . . . . .
229 112 112 . . . . . . . . . . . . . . . . .
230 112 112 . . . . . . . . . . . . . . . . .
231 112 112 . . . . . . . . . . . . . . . . .
232 112 112 . . . . . . . . . . . . . . . . .
233 112 112 . . . . . . . . . . . . . . . . .
234 112 112 . . . . . . . . . . . . . . . . .
235 112 112 . . . . . . . . . . . . . . . . .
236 112 112 . . . . . . . . . . . . . . . . .
237 112 112 . . . . . . . . . . . . . . . . .
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
Exclude 9 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:35
[1] 2019-04-13 03:03:35, OOB Acc: 86.96%, # of SNPs: 11, # of Haplo: 48
[2] 2019-04-13 03:03:35, OOB Acc: 82.61%, # of SNPs: 14, # of Haplo: 54
[3] 2019-04-13 03:03:36, OOB Acc: 97.22%, # of SNPs: 21, # of Haplo: 50
[4] 2019-04-13 03:03:36, OOB Acc: 90.38%, # of SNPs: 16, # of Haplo: 34
[5] 2019-04-13 03:03:36, OOB Acc: 95.45%, # of SNPs: 20, # of Haplo: 38
[6] 2019-04-13 03:03:36, OOB Acc: 95.83%, # of SNPs: 18, # of Haplo: 83
[7] 2019-04-13 03:03:37, OOB Acc: 100.00%, # of SNPs: 24, # of Haplo: 100
[8] 2019-04-13 03:03:37, OOB Acc: 100.00%, # of SNPs: 18, # of Haplo: 48
[9] 2019-04-13 03:03:37, OOB Acc: 94.74%, # of SNPs: 15, # of Haplo: 28
[10] 2019-04-13 03:03:38, OOB Acc: 83.33%, # of SNPs: 12, # of Haplo: 20
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002170206 0.0002218348 0.0002651623 0.0035806631 0.0108171268 0.0260212634
Max. Mean SD
0.4900436399 0.0436176972 0.1079908676
Accuracy with training data: 97.5%
Out-of-bag accuracy: 92.7%
Gene: A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 10
total # of SNPs used: 104
avg. # of SNPs in an individual classifier: 16.90
(sd: 4.09, min: 11, max: 24, median: 17.00)
avg. # of haplotypes in an individual classifier: 50.30
(sd: 24.49, min: 20, max: 100, median: 48.00)
avg. out-of-bag accuracy: 92.65%
(sd: 6.47%, min: 82.61%, max: 100.00%, median: 95.10%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002170206 0.0002218348 0.0002651623 0.0035806631 0.0108171268 0.0260212634
Max. Mean SD
0.4900436399 0.0436176972 0.1079908676
Genome assembly: hg19
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
Loading required namespace: gdsfmt
Loading required namespace: SNPRelate
Import 2348 SNPs from chromosome 6.
3 SNPs with invalid alleles have been removed.
SNP genotypes:
165 samples X 2345 SNPs
SNPs range from 24652946bp to 33524089bp on hg18
Missing rate per SNP:
min: 0, max: 0.0484848, mean: 0.001698, median: 0, sd: 0.00504919
Missing rate per sample:
min: 0, max: 0.0110874, mean: 0.001698, median: 0.000852878, sd: 0.00195105
Minor allele frequency:
min: 0, max: 0.5, mean: 0.194167, median: 0.172727, sd: 0.149319
Allelic information:
A/G T/C G/A C/T T/G A/C C/A G/T C/G G/C A/T T/A
556 467 410 400 116 109 103 81 30 29 28 16
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
No allelic strand orders are switched.
SNP genotypes:
150 samples X 1214 SNPs
SNPs range from 28695148bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0866667, mean: 0.0844646, median: 0.0866667, sd: 0.0128841
Missing rate per sample:
min: 0, max: 0.968699, mean: 0.0844646, median: 0.000823723, sd: 0.273119
Minor allele frequency:
min: 0, max: 0.5, mean: 0.234168, median: 0.218978, sd: 0.137855
Allelic information:
A/G C/T G/T A/C
505 496 109 104
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1197 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0657059, median: 0.0666667, sd: 0.00757446
Missing rate per sample:
min: 0, max: 0.978279, mean: 0.0657059, median: 0.000835422, sd: 0.245786
Minor allele frequency:
min: 0.101695, max: 0.5, mean: 0.278734, median: 0.267857, sd: 0.117338
Allelic information:
A/G C/T A/C G/T
511 476 105 105
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
SNP genotypes:
90 samples X 3932 SNPs
SNPs range from 28694391bp to 33426848bp on hg19
Missing rate per SNP:
min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489
Missing rate per sample:
min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554
Minor allele frequency:
min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144
Allelic information:
A/G C/T G/T A/C C/G A/T
1567 1510 348 332 111 64
No allelic strand orders are switched.
SNP genotypes:
60 samples X 1214 SNPs
SNPs range from 28695148bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0650879, median: 0.0666667, sd: 0.0097381
Missing rate per sample:
min: 0, max: 0.968699, mean: 0.0650879, median: 0.000823723, sd: 0.243373
Minor allele frequency:
min: 0, max: 0.5, mean: 0.234476, median: 0.223214, sd: 0.13833
Allelic information:
A/G C/T G/T A/C
505 496 109 104
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
MAF filter (>=0.01), excluding 9 SNP(s)
using the default genome assembly (assembly="hg19")
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:03:40
[1] 2019-04-13 03:03:40, OOB Acc: 92.00%, # of SNPs: 24, # of Haplo: 29
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.222247e-28 1.128571e-24 1.128371e-23 6.944660e-04 8.333349e-03 3.673611e-02
Max. Mean SD
9.105734e-02 2.054649e-02 2.598603e-02
Accuracy with training data: 96.7%
Out-of-bag accuracy: 92.0%
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:03:40
[1] 2019-04-13 03:03:40, OOB Acc: 97.50%, # of SNPs: 18, # of Haplo: 34
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
5.014366e-13 4.671716e-10 4.667203e-09 1.640727e-03 7.504546e-03 2.126745e-02
Max. Mean SD
9.784316e-02 1.490504e-02 1.947399e-02
Accuracy with training data: 97.5%
Out-of-bag accuracy: 97.5%
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:03:40
[1] 2019-04-13 03:03:40, OOB Acc: 88.89%, # of SNPs: 14, # of Haplo: 38
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.222223e-18 6.603163e-16 6.583163e-15 1.944468e-03 1.020834e-02 4.122739e-02
Max. Mean SD
1.808372e-01 2.422083e-02 3.699146e-02
Accuracy with training data: 95.8%
Out-of-bag accuracy: 88.9%
Gene: C
Training dataset: 60 samples X 83 SNPs
# of HLA alleles: 17
# of individual classifiers: 3
total # of SNPs used: 40
avg. # of SNPs in an individual classifier: 18.67
(sd: 5.03, min: 14, max: 24, median: 18.00)
avg. # of haplotypes in an individual classifier: 33.67
(sd: 4.51, min: 29, max: 38, median: 34.00)
avg. out-of-bag accuracy: 92.80%
(sd: 4.36%, min: 88.89%, max: 97.50%, median: 92.00%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.708707e-13 1.229313e-05 1.229313e-04 1.860746e-03 9.050936e-03 3.332722e-02
Max. Mean SD
1.210500e-01 1.989079e-02 2.507466e-02
Genome assembly: hg19
Exclude 1 monomorphic SNP
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 9
# of SNPs: 77, # of samples: 60
# of unique HLA alleles: 12
[-] 2019-04-13 03:03:40
[1] 2019-04-13 03:03:40, OOB Acc: 98.00%, # of SNPs: 13, # of Haplo: 20
[2] 2019-04-13 03:03:40, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 21
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02
Max. Mean SD
4.728168e-01 4.409445e-02 1.068815e-01
Accuracy with training data: 95.0%
Out-of-bag accuracy: 94.5%
Gene: DQB1
Training dataset: 60 samples X 77 SNPs
# of HLA alleles: 12
# of individual classifiers: 2
total # of SNPs used: 20
avg. # of SNPs in an individual classifier: 14.00
(sd: 1.41, min: 13, max: 15, median: 14.00)
avg. # of haplotypes in an individual classifier: 20.50
(sd: 0.71, min: 20, max: 21, median: 20.50)
avg. out-of-bag accuracy: 94.45%
(sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02
Max. Mean SD
4.728168e-01 4.409445e-02 1.068815e-01
Genome assembly: hg19
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:40
[1] 2019-04-13 03:03:40, OOB Acc: 86.96%, # of SNPs: 11, # of Haplo: 48
[2] 2019-04-13 03:03:41, OOB Acc: 82.61%, # of SNPs: 14, # of Haplo: 54
[3] 2019-04-13 03:03:41, OOB Acc: 97.22%, # of SNPs: 21, # of Haplo: 50
[4] 2019-04-13 03:03:41, OOB Acc: 90.38%, # of SNPs: 16, # of Haplo: 34
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001562613 0.0001654796 0.0002484448 0.0031072694 0.0084676824 0.0267061654
Max. Mean SD
0.5667222637 0.0472474454 0.1286381649
Accuracy with training data: 98.3%
Out-of-bag accuracy: 89.3%
Gene: A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 50
avg. # of SNPs in an individual classifier: 15.50
(sd: 4.20, min: 11, max: 21, median: 15.00)
avg. # of haplotypes in an individual classifier: 46.50
(sd: 8.70, min: 34, max: 54, median: 49.00)
avg. out-of-bag accuracy: 89.29%
(sd: 6.17%, min: 82.61%, max: 97.22%, median: 88.67%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001562613 0.0001654796 0.0002484448 0.0031072694 0.0084676824 0.0267061654
Max. Mean SD
0.5667222637 0.0472474454 0.1286381649
Genome assembly: hg19
Gene: A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 50
avg. # of SNPs in an individual classifier: 15.50
(sd: 4.20, min: 11, max: 21, median: 15.00)
avg. # of haplotypes in an individual classifier: 46.50
(sd: 8.70, min: 34, max: 54, median: 49.00)
avg. out-of-bag accuracy: 89.29%
(sd: 6.17%, min: 82.61%, max: 97.22%, median: 88.67%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001562613 0.0001654796 0.0002484448 0.0031072694 0.0084676824 0.0267061654
Max. Mean SD
0.5667222637 0.0472474454 0.1286381649
Genome assembly: hg19
Sat Apr 13 03:03:41 2019, passing the 1/4 classifiers.
Sat Apr 13 03:03:41 2019, passing the 2/4 classifiers.
Sat Apr 13 03:03:41 2019, passing the 3/4 classifiers.
Sat Apr 13 03:03:41 2019, passing the 4/4 classifiers.
Allele Num. Freq. CR ACC SEN SPE PPV NPV Miscall
Valid. Valid. (%) (%) (%) (%) (%) (%) (%)
----
Overall accuracy: 88.1%, Call rate: 100.0%
01:01 25 0.2083 100.0 99.5 100.0 99.4 95.8 100.0 --
02:01 43 0.3583 100.0 96.1 100.0 94.2 89.0 100.0 --
02:06 1 0.0083 50.0 97.6 0.0 100.0 -- 97.6 02:01 (100)
03:01 9 0.0750 100.0 96.2 93.8 96.4 72.5 99.4 24:02 (100)
11:01 5 0.0417 100.0 100.0 100.0 100.0 100.0 100.0 --
23:01 3 0.0250 75.0 99.4 83.3 100.0 100.0 99.3 24:02 (100)
24:02 11 0.0917 100.0 98.3 91.7 98.9 90.8 99.3 03:01 (100)
24:03 1 0.0083 50.0 97.8 0.0 100.0 -- 97.8 01:01 (50)
25:01 5 0.0417 100.0 98.4 100.0 98.3 84.7 100.0 --
26:01 3 0.0250 100.0 97.8 50.0 100.0 100.0 97.8 25:01 (62)
29:02 4 0.0333 75.0 93.6 33.3 99.3 50.0 94.2 02:01 (50)
31:01 3 0.0250 75.0 100.0 100.0 100.0 100.0 100.0 --
32:01 4 0.0333 50.0 100.0 100.0 100.0 100.0 100.0 --
68:01 3 0.0250 100.0 98.6 75.0 100.0 100.0 98.6 03:01 (67)
\title{Imputation Evaluation}
\documentclass[12pt]{article}
\usepackage{fullpage}
\usepackage{longtable}
\begin{document}
\maketitle
\setlength{\LTcapwidth}{6.5in}
% -------- BEGIN TABLE --------
\begin{longtable}{rrr | rrrrrrl}
\caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).}
\label{tab:accuracy} \\
Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endfirsthead
\multicolumn{10}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\
Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endhead
\hline
\multicolumn{10}{r}{Continued on next page ...} \\
\hline
\endfoot
\hline\hline
\endlastfoot
\multicolumn{10}{l}{\it Overall accuracy: 88.1\%, Call rate: 100.0\%} \\
01:01 & 25 & 0.2083 & 100.0 & 99.5 & 100.0 & 99.4 & 95.8 & 100.0 & -- \\
02:01 & 43 & 0.3583 & 100.0 & 96.1 & 100.0 & 94.2 & 89.0 & 100.0 & -- \\
02:06 & 1 & 0.0083 & 50.0 & 97.6 & 0.0 & 100.0 & -- & 97.6 & 02:01 (100) \\
03:01 & 9 & 0.0750 & 100.0 & 96.2 & 93.8 & 96.4 & 72.5 & 99.4 & 24:02 (100) \\
11:01 & 5 & 0.0417 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
23:01 & 3 & 0.0250 & 75.0 & 99.4 & 83.3 & 100.0 & 100.0 & 99.3 & 24:02 (100) \\
24:02 & 11 & 0.0917 & 100.0 & 98.3 & 91.7 & 98.9 & 90.8 & 99.3 & 03:01 (100) \\
24:03 & 1 & 0.0083 & 50.0 & 97.8 & 0.0 & 100.0 & -- & 97.8 & 01:01 (50) \\
25:01 & 5 & 0.0417 & 100.0 & 98.4 & 100.0 & 98.3 & 84.7 & 100.0 & -- \\
26:01 & 3 & 0.0250 & 100.0 & 97.8 & 50.0 & 100.0 & 100.0 & 97.8 & 25:01 (62) \\
29:02 & 4 & 0.0333 & 75.0 & 93.6 & 33.3 & 99.3 & 50.0 & 94.2 & 02:01 (50) \\
31:01 & 3 & 0.0250 & 75.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
32:01 & 4 & 0.0333 & 50.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
68:01 & 3 & 0.0250 & 100.0 & 98.6 & 75.0 & 100.0 & 100.0 & 98.6 & 03:01 (67) \\
\end{longtable}
% -------- END TABLE --------
\end{document}
<!DOCTYPE html>
<html>
<head>
<title>Imputation Evaluation</title>
</head>
<body>
<h1>Imputation Evaluation</h1>
<p></p>
<h3><b>Table 1L:</b> The sensitivity (SEN), specificity (SPE),
positive predictive value (PPV), negative predictive value (NPV)
and call rate (CR).</h3>
<table id="TB-Acc" class="tabular" border="1" CELLSPACING="1">
<tr>
<th>Allele </th> <th>Num. Valid.</th> <th>Freq. Valid.</th> <th>CR (%)</th> <th>ACC (%)</th> <th>SEN (%)</th> <th>SPE (%)</th> <th>PPV (%)</th> <th>NPV (%)</th> <th>Miscall (%)</th>
</tr>
<tr>
<td colspan="10">
<i> Overall accuracy: 88.1%, Call rate: 100.0% </i>
</td>
</tr>
<tr>
<td>01:01</td> <td>25</td> <td>0.2083</td> <td>100.0</td> <td>99.5</td> <td>100.0</td> <td>99.4</td> <td>95.8</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:01</td> <td>43</td> <td>0.3583</td> <td>100.0</td> <td>96.1</td> <td>100.0</td> <td>94.2</td> <td>89.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:06</td> <td>1</td> <td>0.0083</td> <td>50.0</td> <td>97.6</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>97.6</td> <td>02:01 (100)</td>
</tr>
<tr>
<td>03:01</td> <td>9</td> <td>0.0750</td> <td>100.0</td> <td>96.2</td> <td>93.8</td> <td>96.4</td> <td>72.5</td> <td>99.4</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>11:01</td> <td>5</td> <td>0.0417</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>23:01</td> <td>3</td> <td>0.0250</td> <td>75.0</td> <td>99.4</td> <td>83.3</td> <td>100.0</td> <td>100.0</td> <td>99.3</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>24:02</td> <td>11</td> <td>0.0917</td> <td>100.0</td> <td>98.3</td> <td>91.7</td> <td>98.9</td> <td>90.8</td> <td>99.3</td> <td>03:01 (100)</td>
</tr>
<tr>
<td>24:03</td> <td>1</td> <td>0.0083</td> <td>50.0</td> <td>97.8</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>97.8</td> <td>01:01 (50)</td>
</tr>
<tr>
<td>25:01</td> <td>5</td> <td>0.0417</td> <td>100.0</td> <td>98.4</td> <td>100.0</td> <td>98.3</td> <td>84.7</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>26:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>97.8</td> <td>50.0</td> <td>100.0</td> <td>100.0</td> <td>97.8</td> <td>25:01 (62)</td>
</tr>
<tr>
<td>29:02</td> <td>4</td> <td>0.0333</td> <td>75.0</td> <td>93.6</td> <td>33.3</td> <td>99.3</td> <td>50.0</td> <td>94.2</td> <td>02:01 (50)</td>
</tr>
<tr>
<td>31:01</td> <td>3</td> <td>0.0250</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>32:01</td> <td>4</td> <td>0.0333</td> <td>50.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>68:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>98.6</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>98.6</td> <td>03:01 (67)</td>
</tr>
</table>
</body>
</html>
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 24
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Build a HIBAG model of 4 individual classifiers in parallel with 2 compute nodes:
2019-04-13 03:03:42, 1, job 1, # of SNPs: 15, # of haplo: 29, acc: 68.2%
-- avg out-of-bag acc: 68.18%, sd: NA%, min: 68.18%, max: 68.18%
2019-04-13 03:03:42, 2, job 1, # of SNPs: 11, # of haplo: 20, acc: 75.0%
-- avg out-of-bag acc: 71.59%, sd: 4.82%, min: 68.18%, max: 75.00%
2019-04-13 03:03:42, 3, job 2, # of SNPs: 13, # of haplo: 25, acc: 77.3%
-- avg out-of-bag acc: 73.48%, sd: 4.73%, min: 68.18%, max: 77.27%
2019-04-13 03:03:42, 4, job 1, # of SNPs: 12, # of haplo: 23, acc: 89.3%
-- avg out-of-bag acc: 77.44%, sd: 8.79%, min: 68.18%, max: 89.29%
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0006488053 0.0006523782 0.0006845350 0.0050013866 0.0110310747 0.0298617344
Max. Mean SD
0.3392277674 0.0432960091 0.0837228980
Accuracy with training data: 97.1%
Out-of-bag accuracy: 77.4%
Build a HIBAG model of 4 individual classifiers in parallel with 2 compute nodes:
The model is autosaved in 'tmp_model.RData'.
2019-04-13 03:03:43, 1, job 2, # of SNPs: 15, # of haplo: 28, acc: 79.2%
-- avg out-of-bag acc: 79.17%, sd: NA%, min: 79.17%, max: 79.17%
2019-04-13 03:03:43, 2, job 2, # of SNPs: 12, # of haplo: 40, acc: 87.5%
-- avg out-of-bag acc: 83.33%, sd: 5.89%, min: 79.17%, max: 87.50%
2019-04-13 03:03:43, 3, job 1, # of SNPs: 13, # of haplo: 46, acc: 87.5%
Stop "job 1".
-- avg out-of-bag acc: 84.72%, sd: 4.81%, min: 79.17%, max: 87.50%
2019-04-13 03:03:43, 4, job 1, # of SNPs: 11, # of haplo: 19, acc: 86.4%
Stop "job 1".
-- avg out-of-bag acc: 85.13%, sd: 4.01%, min: 79.17%, max: 87.50%
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004325303 0.0004325306 0.0004325332 0.0045836653 0.0086680504 0.0199862786
Max. Mean SD
0.4708241730 0.0410341661 0.1073727492
Accuracy with training data: 94.1%
Out-of-bag accuracy: 85.1%
Gene: A
Training dataset: 34 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 41
avg. # of SNPs in an individual classifier: 12.75
(sd: 1.71, min: 11, max: 15, median: 12.50)
avg. # of haplotypes in an individual classifier: 33.25
(sd: 12.09, min: 19, max: 46, median: 34.00)
avg. out-of-bag accuracy: 85.13%
(sd: 4.01%, min: 79.17%, max: 87.50%, median: 86.93%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004325303 0.0004325306 0.0004325332 0.0045836653 0.0086680504 0.0199862786
Max. Mean SD
0.4708241730 0.0410341661 0.1073727492
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:43) 0%
Predicting (2019-04-13 03:03:43) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 15
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 4 (15.4%) 5 (19.2%) 16 (61.5%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000001 0.0018773 0.0062051 0.0290716 0.0230480 0.4708242
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Run in parallel with 2 compute nodes.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 15
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 4 (15.4%) 5 (19.2%) 16 (61.5%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000001 0.0018773 0.0062051 0.0290716 0.0230480 0.4708242
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:43
1, SNP: 123, Loss: 203.122, OOB Acc: 54.55%, # of Haplo: 14
2, SNP: 182, Loss: 155.176, OOB Acc: 63.64%, # of Haplo: 16
3, SNP: 204, Loss: 130.453, OOB Acc: 72.73%, # of Haplo: 16
4, SNP: 87, Loss: 81.1116, OOB Acc: 77.27%, # of Haplo: 20
5, SNP: 115, Loss: 60.8965, OOB Acc: 86.36%, # of Haplo: 21
6, SNP: 191, Loss: 35.3274, OOB Acc: 90.91%, # of Haplo: 22
7, SNP: 220, Loss: 23.2514, OOB Acc: 90.91%, # of Haplo: 24
8, SNP: 47, Loss: 19.9968, OOB Acc: 90.91%, # of Haplo: 25
9, SNP: 20, Loss: 18.0597, OOB Acc: 90.91%, # of Haplo: 27
10, SNP: 171, Loss: 17.3174, OOB Acc: 90.91%, # of Haplo: 27
11, SNP: 158, Loss: 16.9514, OOB Acc: 90.91%, # of Haplo: 29
12, SNP: 247, Loss: 15.5658, OOB Acc: 90.91%, # of Haplo: 35
13, SNP: 193, Loss: 15.4994, OOB Acc: 90.91%, # of Haplo: 44
14, SNP: 73, Loss: 12.4986, OOB Acc: 90.91%, # of Haplo: 45
15, SNP: 199, Loss: 11.7779, OOB Acc: 90.91%, # of Haplo: 45
16, SNP: 151, Loss: 11.0487, OOB Acc: 90.91%, # of Haplo: 45
[1] 2019-04-13 03:03:43, OOB Acc: 90.91%, # of SNPs: 16, # of Haplo: 45
1, SNP: 171, Loss: 169.639, OOB Acc: 56.67%, # of Haplo: 14
2, SNP: 127, Loss: 146.326, OOB Acc: 63.33%, # of Haplo: 16
3, SNP: 109, Loss: 104.178, OOB Acc: 66.67%, # of Haplo: 18
4, SNP: 107, Loss: 90.6768, OOB Acc: 73.33%, # of Haplo: 20
5, SNP: 163, Loss: 66.9122, OOB Acc: 76.67%, # of Haplo: 25
6, SNP: 185, Loss: 60.1057, OOB Acc: 76.67%, # of Haplo: 33
7, SNP: 99, Loss: 55.2312, OOB Acc: 76.67%, # of Haplo: 39
8, SNP: 199, Loss: 47.686, OOB Acc: 76.67%, # of Haplo: 39
9, SNP: 95, Loss: 46.9138, OOB Acc: 80.00%, # of Haplo: 47
10, SNP: 70, Loss: 45.9845, OOB Acc: 80.00%, # of Haplo: 49
11, SNP: 129, Loss: 45.86, OOB Acc: 83.33%, # of Haplo: 49
[2] 2019-04-13 03:03:43, OOB Acc: 83.33%, # of SNPs: 11, # of Haplo: 49
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.677486e-08 3.642759e-06 3.573661e-05 1.908829e-03 6.558496e-03 2.050541e-02
Max. Mean SD
5.935371e-01 5.215016e-02 1.400728e-01
Accuracy with training data: 97.1%
Out-of-bag accuracy: 87.1%
Exclude 9 monomorphic SNPs
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:43
1, SNP: 122, Loss: 162.121, OOB Acc: 50.00%, # of Haplo: 12
2, SNP: 137, Loss: 155.367, OOB Acc: 58.33%, # of Haplo: 12
3, SNP: 81, Loss: 120.826, OOB Acc: 58.33%, # of Haplo: 16
4, SNP: 146, Loss: 83.2015, OOB Acc: 62.50%, # of Haplo: 21
5, SNP: 148, Loss: 68.8368, OOB Acc: 62.50%, # of Haplo: 21
6, SNP: 51, Loss: 40.3043, OOB Acc: 62.50%, # of Haplo: 21
7, SNP: 231, Loss: 29.1968, OOB Acc: 62.50%, # of Haplo: 23
8, SNP: 87, Loss: 23.3916, OOB Acc: 62.50%, # of Haplo: 31
9, SNP: 160, Loss: 20.422, OOB Acc: 62.50%, # of Haplo: 31
10, SNP: 119, Loss: 20.2055, OOB Acc: 62.50%, # of Haplo: 32
11, SNP: 9, Loss: 19.9994, OOB Acc: 62.50%, # of Haplo: 37
12, SNP: 199, Loss: 19.2624, OOB Acc: 62.50%, # of Haplo: 38
13, SNP: 136, Loss: 19.0415, OOB Acc: 62.50%, # of Haplo: 40
14, SNP: 151, Loss: 18.6147, OOB Acc: 62.50%, # of Haplo: 40
15, SNP: 3, Loss: 18.5586, OOB Acc: 62.50%, # of Haplo: 45
16, SNP: 73, Loss: 14.2585, OOB Acc: 62.50%, # of Haplo: 46
[1] 2019-04-13 03:03:44, OOB Acc: 62.50%, # of SNPs: 16, # of Haplo: 46
1, SNP: 122, Loss: 185.298, OOB Acc: 75.00%, # of Haplo: 13
2, SNP: 118, Loss: 178.626, OOB Acc: 83.33%, # of Haplo: 13
3, SNP: 131, Loss: 149.969, OOB Acc: 87.50%, # of Haplo: 14
4, SNP: 144, Loss: 97.8546, OOB Acc: 87.50%, # of Haplo: 14
5, SNP: 123, Loss: 62.1051, OOB Acc: 91.67%, # of Haplo: 14
6, SNP: 105, Loss: 48.1095, OOB Acc: 95.83%, # of Haplo: 14
7, SNP: 197, Loss: 30.2151, OOB Acc: 95.83%, # of Haplo: 16
8, SNP: 40, Loss: 28.3663, OOB Acc: 95.83%, # of Haplo: 20
9, SNP: 90, Loss: 24.3188, OOB Acc: 95.83%, # of Haplo: 22
10, SNP: 263, Loss: 19.7509, OOB Acc: 95.83%, # of Haplo: 31
11, SNP: 67, Loss: 15.2521, OOB Acc: 95.83%, # of Haplo: 31
12, SNP: 199, Loss: 14.3355, OOB Acc: 95.83%, # of Haplo: 33
13, SNP: 73, Loss: 9.42737, OOB Acc: 95.83%, # of Haplo: 33
[2] 2019-04-13 03:03:44, OOB Acc: 95.83%, # of SNPs: 13, # of Haplo: 33
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
6.055448e-13 1.784206e-06 1.784206e-05 1.513906e-03 5.745114e-03 1.789755e-02
Max. Mean SD
3.732254e-01 3.556249e-02 8.857257e-02
Accuracy with training data: 94.1%
Out-of-bag accuracy: 79.2%
HIBAG model: 2 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting by voting from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:44) 0%
Predicting (2019-04-13 03:03:44) 100%
HIBAG model: 2 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting by voting from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:44) 0%
Predicting (2019-04-13 03:03:44) 100%
Exclude 1 monomorphic SNP
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 13
# of SNPs: 158, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:44
1, SNP: 141, Loss: 378.06, OOB Acc: 52.08%, # of Haplo: 14
2, SNP: 74, Loss: 262.497, OOB Acc: 58.33%, # of Haplo: 15
3, SNP: 78, Loss: 162.497, OOB Acc: 68.75%, # of Haplo: 19
4, SNP: 118, Loss: 70.0426, OOB Acc: 72.92%, # of Haplo: 23
5, SNP: 82, Loss: 45.8279, OOB Acc: 83.33%, # of Haplo: 23
6, SNP: 95, Loss: 35.4414, OOB Acc: 89.58%, # of Haplo: 27
7, SNP: 89, Loss: 32.6134, OOB Acc: 89.58%, # of Haplo: 35
8, SNP: 83, Loss: 31.7921, OOB Acc: 89.58%, # of Haplo: 51
9, SNP: 151, Loss: 31.0653, OOB Acc: 89.58%, # of Haplo: 55
10, SNP: 94, Loss: 31.0246, OOB Acc: 89.58%, # of Haplo: 55
11, SNP: 111, Loss: 18.9027, OOB Acc: 89.58%, # of Haplo: 56
12, SNP: 139, Loss: 18.4248, OOB Acc: 89.58%, # of Haplo: 59
13, SNP: 93, Loss: 17.0195, OOB Acc: 91.67%, # of Haplo: 58
14, SNP: 15, Loss: 14.1692, OOB Acc: 91.67%, # of Haplo: 60
[1] 2019-04-13 03:03:44, OOB Acc: 91.67%, # of SNPs: 14, # of Haplo: 60
1, SNP: 94, Loss: 403.365, OOB Acc: 63.16%, # of Haplo: 15
2, SNP: 82, Loss: 294.053, OOB Acc: 71.05%, # of Haplo: 18
3, SNP: 57, Loss: 226.142, OOB Acc: 76.32%, # of Haplo: 23
4, SNP: 155, Loss: 197.199, OOB Acc: 84.21%, # of Haplo: 29
5, SNP: 44, Loss: 132.804, OOB Acc: 86.84%, # of Haplo: 40
6, SNP: 30, Loss: 122.507, OOB Acc: 92.11%, # of Haplo: 40
7, SNP: 109, Loss: 72.0179, OOB Acc: 92.11%, # of Haplo: 41
8, SNP: 72, Loss: 59.3281, OOB Acc: 92.11%, # of Haplo: 41
9, SNP: 36, Loss: 54.939, OOB Acc: 94.74%, # of Haplo: 43
10, SNP: 127, Loss: 48.1392, OOB Acc: 94.74%, # of Haplo: 43
11, SNP: 53, Loss: 44.7676, OOB Acc: 94.74%, # of Haplo: 43
12, SNP: 148, Loss: 43.047, OOB Acc: 94.74%, # of Haplo: 44
13, SNP: 152, Loss: 40.2104, OOB Acc: 94.74%, # of Haplo: 45
14, SNP: 125, Loss: 39.8862, OOB Acc: 94.74%, # of Haplo: 45
15, SNP: 78, Loss: 39.5652, OOB Acc: 94.74%, # of Haplo: 45
16, SNP: 3, Loss: 39.0621, OOB Acc: 94.74%, # of Haplo: 47
17, SNP: 141, Loss: 37.6822, OOB Acc: 94.74%, # of Haplo: 47
18, SNP: 1, Loss: 36.5022, OOB Acc: 94.74%, # of Haplo: 50
[2] 2019-04-13 03:03:44, OOB Acc: 94.74%, # of SNPs: 18, # of Haplo: 50
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02
Max. Mean SD
5.948644e-01 6.324378e-02 1.404915e-01
Accuracy with training data: 96.7%
Out-of-bag accuracy: 93.2%
Gene: A
Training dataset: 60 samples X 158 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 28
avg. # of SNPs in an individual classifier: 16.00
(sd: 2.83, min: 14, max: 18, median: 16.00)
avg. # of haplotypes in an individual classifier: 55.00
(sd: 7.07, min: 50, max: 60, median: 55.00)
avg. out-of-bag accuracy: 93.20%
(sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02
Max. Mean SD
5.948644e-01 6.324378e-02 1.404915e-01
Genome assembly: hg19
Remove 130 unused SNPs.
Gene: A
Training dataset: 60 samples X 28 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 28
avg. # of SNPs in an individual classifier: 16.00
(sd: 2.83, min: 14, max: 18, median: 16.00)
avg. # of haplotypes in an individual classifier: 55.00
(sd: 7.07, min: 50, max: 60, median: 55.00)
avg. out-of-bag accuracy: 93.20%
(sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02
Max. Mean SD
5.948644e-01 6.324378e-02 1.404915e-01
Genome assembly: hg19
Platform: Illumina 1M Duo
Information: Training set -- HapMap Phase II
HIBAG model: 2 individual classifiers, 158 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 60
Predicting (2019-04-13 03:03:44) 0%
Predicting (2019-04-13 03:03:44) 100%
HIBAG model: 2 individual classifiers, 28 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 60
Predicting (2019-04-13 03:03:44) 0%
Predicting (2019-04-13 03:03:44) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:44
1, SNP: 123, Loss: 203.122, OOB Acc: 54.55%, # of Haplo: 14
2, SNP: 182, Loss: 155.176, OOB Acc: 63.64%, # of Haplo: 16
3, SNP: 204, Loss: 130.453, OOB Acc: 72.73%, # of Haplo: 16
4, SNP: 87, Loss: 81.1116, OOB Acc: 77.27%, # of Haplo: 20
5, SNP: 115, Loss: 60.8965, OOB Acc: 86.36%, # of Haplo: 21
6, SNP: 191, Loss: 35.3274, OOB Acc: 90.91%, # of Haplo: 22
7, SNP: 220, Loss: 23.2514, OOB Acc: 90.91%, # of Haplo: 24
8, SNP: 47, Loss: 19.9968, OOB Acc: 90.91%, # of Haplo: 25
9, SNP: 20, Loss: 18.0597, OOB Acc: 90.91%, # of Haplo: 27
10, SNP: 171, Loss: 17.3174, OOB Acc: 90.91%, # of Haplo: 27
11, SNP: 158, Loss: 16.9514, OOB Acc: 90.91%, # of Haplo: 29
12, SNP: 247, Loss: 15.5658, OOB Acc: 90.91%, # of Haplo: 35
13, SNP: 193, Loss: 15.4994, OOB Acc: 90.91%, # of Haplo: 44
14, SNP: 73, Loss: 12.4986, OOB Acc: 90.91%, # of Haplo: 45
15, SNP: 199, Loss: 11.7779, OOB Acc: 90.91%, # of Haplo: 45
16, SNP: 151, Loss: 11.0487, OOB Acc: 90.91%, # of Haplo: 45
[1] 2019-04-13 03:03:44, OOB Acc: 90.91%, # of SNPs: 16, # of Haplo: 45
1, SNP: 171, Loss: 169.639, OOB Acc: 56.67%, # of Haplo: 14
2, SNP: 127, Loss: 146.326, OOB Acc: 63.33%, # of Haplo: 16
3, SNP: 109, Loss: 104.178, OOB Acc: 66.67%, # of Haplo: 18
4, SNP: 107, Loss: 90.6768, OOB Acc: 73.33%, # of Haplo: 20
5, SNP: 163, Loss: 66.9122, OOB Acc: 76.67%, # of Haplo: 25
6, SNP: 185, Loss: 60.1057, OOB Acc: 76.67%, # of Haplo: 33
7, SNP: 99, Loss: 55.2312, OOB Acc: 76.67%, # of Haplo: 39
8, SNP: 199, Loss: 47.686, OOB Acc: 76.67%, # of Haplo: 39
9, SNP: 95, Loss: 46.9138, OOB Acc: 80.00%, # of Haplo: 47
10, SNP: 70, Loss: 45.9845, OOB Acc: 80.00%, # of Haplo: 49
11, SNP: 129, Loss: 45.86, OOB Acc: 83.33%, # of Haplo: 49
[2] 2019-04-13 03:03:44, OOB Acc: 83.33%, # of SNPs: 11, # of Haplo: 49
1, SNP: 122, Loss: 162.121, OOB Acc: 50.00%, # of Haplo: 12
2, SNP: 137, Loss: 155.367, OOB Acc: 58.33%, # of Haplo: 12
3, SNP: 81, Loss: 120.826, OOB Acc: 58.33%, # of Haplo: 16
4, SNP: 146, Loss: 83.2015, OOB Acc: 62.50%, # of Haplo: 21
5, SNP: 148, Loss: 68.8368, OOB Acc: 62.50%, # of Haplo: 21
6, SNP: 51, Loss: 40.3043, OOB Acc: 62.50%, # of Haplo: 21
7, SNP: 231, Loss: 29.1968, OOB Acc: 62.50%, # of Haplo: 23
8, SNP: 87, Loss: 23.3916, OOB Acc: 62.50%, # of Haplo: 31
9, SNP: 160, Loss: 20.422, OOB Acc: 62.50%, # of Haplo: 31
10, SNP: 119, Loss: 20.2055, OOB Acc: 62.50%, # of Haplo: 32
11, SNP: 9, Loss: 19.9994, OOB Acc: 62.50%, # of Haplo: 37
12, SNP: 199, Loss: 19.2624, OOB Acc: 62.50%, # of Haplo: 38
13, SNP: 136, Loss: 19.0415, OOB Acc: 62.50%, # of Haplo: 40
14, SNP: 151, Loss: 18.6147, OOB Acc: 62.50%, # of Haplo: 40
15, SNP: 3, Loss: 18.5586, OOB Acc: 62.50%, # of Haplo: 45
16, SNP: 73, Loss: 14.2585, OOB Acc: 62.50%, # of Haplo: 46
[3] 2019-04-13 03:03:45, OOB Acc: 62.50%, # of SNPs: 16, # of Haplo: 46
1, SNP: 122, Loss: 185.298, OOB Acc: 75.00%, # of Haplo: 13
2, SNP: 118, Loss: 178.626, OOB Acc: 83.33%, # of Haplo: 13
3, SNP: 131, Loss: 149.969, OOB Acc: 87.50%, # of Haplo: 14
4, SNP: 144, Loss: 97.8546, OOB Acc: 87.50%, # of Haplo: 14
5, SNP: 123, Loss: 62.1051, OOB Acc: 91.67%, # of Haplo: 14
6, SNP: 105, Loss: 48.1095, OOB Acc: 95.83%, # of Haplo: 14
7, SNP: 197, Loss: 30.2151, OOB Acc: 95.83%, # of Haplo: 16
8, SNP: 40, Loss: 28.3663, OOB Acc: 95.83%, # of Haplo: 20
9, SNP: 90, Loss: 24.3188, OOB Acc: 95.83%, # of Haplo: 22
10, SNP: 263, Loss: 19.7509, OOB Acc: 95.83%, # of Haplo: 31
11, SNP: 67, Loss: 15.2521, OOB Acc: 95.83%, # of Haplo: 31
12, SNP: 199, Loss: 14.3355, OOB Acc: 95.83%, # of Haplo: 33
13, SNP: 73, Loss: 9.42737, OOB Acc: 95.83%, # of Haplo: 33
[4] 2019-04-13 03:03:45, OOB Acc: 95.83%, # of SNPs: 13, # of Haplo: 33
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.110187e-05 8.734649e-05 1.435481e-04 3.012736e-03 6.600797e-03 4.079281e-02
Max. Mean SD
4.833812e-01 4.385633e-02 1.130754e-01
Accuracy with training data: 97.1%
Out-of-bag accuracy: 83.1%
Gene: A
Training dataset: 34 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 46
avg. # of SNPs in an individual classifier: 14.00
(sd: 2.45, min: 11, max: 16, median: 14.50)
avg. # of haplotypes in an individual classifier: 43.25
(sd: 7.04, min: 33, max: 49, median: 45.50)
avg. out-of-bag accuracy: 83.14%
(sd: 14.69%, min: 62.50%, max: 95.83%, median: 87.12%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.110187e-05 8.734649e-05 1.435481e-04 3.012736e-03 6.600797e-03 4.079281e-02
Max. Mean SD
4.833812e-01 4.385633e-02 1.130754e-01
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:45) 0%
Predicting (2019-04-13 03:03:45) 100%
Allele Num. Freq. Num. Freq. CR ACC SEN SPE PPV NPV Miscall
Train Train Valid. Valid. (%) (%) (%) (%) (%) (%) (%)
----
Overall accuracy: 94.2%, Call rate: 100.0%
01:01 13 0.1912 12 0.2308 100.0 100.0 100.0 100.0 100.0 100.0 --
02:01 23 0.3382 20 0.3846 100.0 100.0 100.0 100.0 100.0 100.0 --
02:06 1 0.0147 0 0 -- -- -- -- -- -- --
03:01 4 0.0588 5 0.0962 100.0 98.1 80.0 100.0 100.0 97.9 24:02 (100)
11:01 3 0.0441 2 0.0385 100.0 100.0 100.0 100.0 100.0 100.0 --
23:01 2 0.0294 1 0.0192 100.0 98.1 100.0 98.0 50.0 100.0 --
24:02 6 0.0882 5 0.0962 100.0 96.2 80.0 97.9 80.0 97.9 23:01 (100)
24:03 1 0.0147 0 0 -- -- -- -- -- -- --
25:01 3 0.0441 2 0.0385 100.0 98.1 50.0 100.0 100.0 98.0 26:01 (100)
26:01 2 0.0294 1 0.0192 100.0 98.1 100.0 98.0 50.0 100.0 --
29:02 3 0.0441 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
31:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
32:01 3 0.0441 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
68:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
\title{Imputation Evaluation}
\documentclass[12pt]{article}
\usepackage{fullpage}
\usepackage{longtable}
\begin{document}
\maketitle
\setlength{\LTcapwidth}{6.5in}
% -------- BEGIN TABLE --------
\begin{longtable}{rrrrr | rrrrrrl}
\caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).}
\label{tab:accuracy} \\
Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endfirsthead
\multicolumn{12}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\
Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endhead
\hline
\multicolumn{12}{r}{Continued on next page ...} \\
\hline
\endfoot
\hline\hline
\endlastfoot
\multicolumn{12}{l}{\it Overall accuracy: 94.2\%, Call rate: 100.0\%} \\
01:01 & 13 & 0.1912 & 12 & 0.2308 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
02:01 & 23 & 0.3382 & 20 & 0.3846 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
02:06 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
03:01 & 4 & 0.0588 & 5 & 0.0962 & 100.0 & 98.1 & 80.0 & 100.0 & 100.0 & 97.9 & 24:02 (100) \\
11:01 & 3 & 0.0441 & 2 & 0.0385 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
23:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 98.1 & 100.0 & 98.0 & 50.0 & 100.0 & -- \\
24:02 & 6 & 0.0882 & 5 & 0.0962 & 100.0 & 96.2 & 80.0 & 97.9 & 80.0 & 97.9 & 23:01 (100) \\
24:03 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
25:01 & 3 & 0.0441 & 2 & 0.0385 & 100.0 & 98.1 & 50.0 & 100.0 & 100.0 & 98.0 & 26:01 (100) \\
26:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 98.1 & 100.0 & 98.0 & 50.0 & 100.0 & -- \\
29:02 & 3 & 0.0441 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
31:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
32:01 & 3 & 0.0441 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
68:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
\end{longtable}
% -------- END TABLE --------
\end{document}
<!DOCTYPE html>
<html>
<head>
<title>Imputation Evaluation</title>
</head>
<body>
<h1>Imputation Evaluation</h1>
<p></p>
<h3><b>Table 1L:</b> The sensitivity (SEN), specificity (SPE),
positive predictive value (PPV), negative predictive value (NPV)
and call rate (CR).</h3>
<table id="TB-Acc" class="tabular" border="1" CELLSPACING="1">
<tr>
<th>Allele </th> <th>Num. Train</th> <th>Freq. Train</th> <th>Num. Valid.</th> <th>Freq. Valid.</th> <th>CR (%)</th> <th>ACC (%)</th> <th>SEN (%)</th> <th>SPE (%)</th> <th>PPV (%)</th> <th>NPV (%)</th> <th>Miscall (%)</th>
</tr>
<tr>
<td colspan="12">
<i> Overall accuracy: 94.2%, Call rate: 100.0% </i>
</td>
</tr>
<tr>
<td>01:01</td> <td>13</td> <td>0.1912</td> <td>12</td> <td>0.2308</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:01</td> <td>23</td> <td>0.3382</td> <td>20</td> <td>0.3846</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:06</td> <td>1</td> <td>0.0147</td> <td>0</td> <td>0</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td>
</tr>
<tr>
<td>03:01</td> <td>4</td> <td>0.0588</td> <td>5</td> <td>0.0962</td> <td>100.0</td> <td>98.1</td> <td>80.0</td> <td>100.0</td> <td>100.0</td> <td>97.9</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>11:01</td> <td>3</td> <td>0.0441</td> <td>2</td> <td>0.0385</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>23:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>98.0</td> <td>50.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>24:02</td> <td>6</td> <td>0.0882</td> <td>5</td> <td>0.0962</td> <td>100.0</td> <td>96.2</td> <td>80.0</td> <td>97.9</td> <td>80.0</td> <td>97.9</td> <td>23:01 (100)</td>
</tr>
<tr>
<td>24:03</td> <td>1</td> <td>0.0147</td> <td>0</td> <td>0</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td>
</tr>
<tr>
<td>25:01</td> <td>3</td> <td>0.0441</td> <td>2</td> <td>0.0385</td> <td>100.0</td> <td>98.1</td> <td>50.0</td> <td>100.0</td> <td>100.0</td> <td>98.0</td> <td>26:01 (100)</td>
</tr>
<tr>
<td>26:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>98.0</td> <td>50.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>29:02</td> <td>3</td> <td>0.0441</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>31:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>32:01</td> <td>3</td> <td>0.0441</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>68:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
</table>
</body>
</html>
**Overall accuracy: 94.2%, Call rate: 100.0%**
| Allele | # Train | Freq. Train | # Valid. | Freq. Valid. | CR (%) | ACC (%) | SEN (%) | SPE (%) | PPV (%) | NPV (%) | Miscall (%) |
|:--|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|:--|
| 01:01 | 13 | 0.1912 | 12 | 0.2308 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 02:01 | 23 | 0.3382 | 20 | 0.3846 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 02:06 | 1 | 0.0147 | 0 | 0 | -- | -- | -- | -- | -- | -- | -- |
| 03:01 | 4 | 0.0588 | 5 | 0.0962 | 100.0 | 98.1 | 80.0 | 100.0 | 100.0 | 97.9 | 24:02 (100) |
| 11:01 | 3 | 0.0441 | 2 | 0.0385 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 23:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 98.1 | 100.0 | 98.0 | 50.0 | 100.0 | -- |
| 24:02 | 6 | 0.0882 | 5 | 0.0962 | 100.0 | 96.2 | 80.0 | 97.9 | 80.0 | 97.9 | 23:01 (100) |
| 24:03 | 1 | 0.0147 | 0 | 0 | -- | -- | -- | -- | -- | -- | -- |
| 25:01 | 3 | 0.0441 | 2 | 0.0385 | 100.0 | 98.1 | 50.0 | 100.0 | 100.0 | 98.0 | 26:01 (100) |
| 26:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 98.1 | 100.0 | 98.0 | 50.0 | 100.0 | -- |
| 29:02 | 3 | 0.0441 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 31:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 32:01 | 3 | 0.0441 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 68:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:45
1, SNP: 123, Loss: 203.122, OOB Acc: 54.55%, # of Haplo: 14
2, SNP: 182, Loss: 155.176, OOB Acc: 63.64%, # of Haplo: 16
3, SNP: 204, Loss: 130.453, OOB Acc: 72.73%, # of Haplo: 16
4, SNP: 87, Loss: 81.1116, OOB Acc: 77.27%, # of Haplo: 20
5, SNP: 115, Loss: 60.8965, OOB Acc: 86.36%, # of Haplo: 21
6, SNP: 191, Loss: 35.3274, OOB Acc: 90.91%, # of Haplo: 22
7, SNP: 220, Loss: 23.2514, OOB Acc: 90.91%, # of Haplo: 24
8, SNP: 47, Loss: 19.9968, OOB Acc: 90.91%, # of Haplo: 25
9, SNP: 20, Loss: 18.0597, OOB Acc: 90.91%, # of Haplo: 27
10, SNP: 171, Loss: 17.3174, OOB Acc: 90.91%, # of Haplo: 27
11, SNP: 158, Loss: 16.9514, OOB Acc: 90.91%, # of Haplo: 29
12, SNP: 247, Loss: 15.5658, OOB Acc: 90.91%, # of Haplo: 35
13, SNP: 193, Loss: 15.4994, OOB Acc: 90.91%, # of Haplo: 44
14, SNP: 73, Loss: 12.4986, OOB Acc: 90.91%, # of Haplo: 45
15, SNP: 199, Loss: 11.7779, OOB Acc: 90.91%, # of Haplo: 45
16, SNP: 151, Loss: 11.0487, OOB Acc: 90.91%, # of Haplo: 45
[1] 2019-04-13 03:03:45, OOB Acc: 90.91%, # of SNPs: 16, # of Haplo: 45
1, SNP: 171, Loss: 169.639, OOB Acc: 56.67%, # of Haplo: 14
2, SNP: 127, Loss: 146.326, OOB Acc: 63.33%, # of Haplo: 16
3, SNP: 109, Loss: 104.178, OOB Acc: 66.67%, # of Haplo: 18
4, SNP: 107, Loss: 90.6768, OOB Acc: 73.33%, # of Haplo: 20
5, SNP: 163, Loss: 66.9122, OOB Acc: 76.67%, # of Haplo: 25
6, SNP: 185, Loss: 60.1057, OOB Acc: 76.67%, # of Haplo: 33
7, SNP: 99, Loss: 55.2312, OOB Acc: 76.67%, # of Haplo: 39
8, SNP: 199, Loss: 47.686, OOB Acc: 76.67%, # of Haplo: 39
9, SNP: 95, Loss: 46.9138, OOB Acc: 80.00%, # of Haplo: 47
10, SNP: 70, Loss: 45.9845, OOB Acc: 80.00%, # of Haplo: 49
11, SNP: 129, Loss: 45.86, OOB Acc: 83.33%, # of Haplo: 49
[2] 2019-04-13 03:03:45, OOB Acc: 83.33%, # of SNPs: 11, # of Haplo: 49
1, SNP: 122, Loss: 162.121, OOB Acc: 50.00%, # of Haplo: 12
2, SNP: 137, Loss: 155.367, OOB Acc: 58.33%, # of Haplo: 12
3, SNP: 81, Loss: 120.826, OOB Acc: 58.33%, # of Haplo: 16
4, SNP: 146, Loss: 83.2015, OOB Acc: 62.50%, # of Haplo: 21
5, SNP: 148, Loss: 68.8368, OOB Acc: 62.50%, # of Haplo: 21
6, SNP: 51, Loss: 40.3043, OOB Acc: 62.50%, # of Haplo: 21
7, SNP: 231, Loss: 29.1968, OOB Acc: 62.50%, # of Haplo: 23
8, SNP: 87, Loss: 23.3916, OOB Acc: 62.50%, # of Haplo: 31
9, SNP: 160, Loss: 20.422, OOB Acc: 62.50%, # of Haplo: 31
10, SNP: 119, Loss: 20.2055, OOB Acc: 62.50%, # of Haplo: 32
11, SNP: 9, Loss: 19.9994, OOB Acc: 62.50%, # of Haplo: 37
12, SNP: 199, Loss: 19.2624, OOB Acc: 62.50%, # of Haplo: 38
13, SNP: 136, Loss: 19.0415, OOB Acc: 62.50%, # of Haplo: 40
14, SNP: 151, Loss: 18.6147, OOB Acc: 62.50%, # of Haplo: 40
15, SNP: 3, Loss: 18.5586, OOB Acc: 62.50%, # of Haplo: 45
16, SNP: 73, Loss: 14.2585, OOB Acc: 62.50%, # of Haplo: 46
[3] 2019-04-13 03:03:45, OOB Acc: 62.50%, # of SNPs: 16, # of Haplo: 46
1, SNP: 122, Loss: 185.298, OOB Acc: 75.00%, # of Haplo: 13
2, SNP: 118, Loss: 178.626, OOB Acc: 83.33%, # of Haplo: 13
3, SNP: 131, Loss: 149.969, OOB Acc: 87.50%, # of Haplo: 14
4, SNP: 144, Loss: 97.8546, OOB Acc: 87.50%, # of Haplo: 14
5, SNP: 123, Loss: 62.1051, OOB Acc: 91.67%, # of Haplo: 14
6, SNP: 105, Loss: 48.1095, OOB Acc: 95.83%, # of Haplo: 14
7, SNP: 197, Loss: 30.2151, OOB Acc: 95.83%, # of Haplo: 16
8, SNP: 40, Loss: 28.3663, OOB Acc: 95.83%, # of Haplo: 20
9, SNP: 90, Loss: 24.3188, OOB Acc: 95.83%, # of Haplo: 22
10, SNP: 263, Loss: 19.7509, OOB Acc: 95.83%, # of Haplo: 31
11, SNP: 67, Loss: 15.2521, OOB Acc: 95.83%, # of Haplo: 31
12, SNP: 199, Loss: 14.3355, OOB Acc: 95.83%, # of Haplo: 33
13, SNP: 73, Loss: 9.42737, OOB Acc: 95.83%, # of Haplo: 33
[4] 2019-04-13 03:03:45, OOB Acc: 95.83%, # of SNPs: 13, # of Haplo: 33
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.110187e-05 8.734649e-05 1.435481e-04 3.012736e-03 6.600797e-03 4.079281e-02
Max. Mean SD
4.833812e-01 4.385633e-02 1.130754e-01
Accuracy with training data: 97.1%
Out-of-bag accuracy: 83.1%
Gene: A
Training dataset: 34 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 46
avg. # of SNPs in an individual classifier: 14.00
(sd: 2.45, min: 11, max: 16, median: 14.50)
avg. # of haplotypes in an individual classifier: 43.25
(sd: 7.04, min: 33, max: 49, median: 45.50)
avg. out-of-bag accuracy: 83.14%
(sd: 14.69%, min: 62.50%, max: 95.83%, median: 87.12%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.110187e-05 8.734649e-05 1.435481e-04 3.012736e-03 6.600797e-03 4.079281e-02
Max. Mean SD
4.833812e-01 4.385633e-02 1.130754e-01
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:45) 0%
Predicting (2019-04-13 03:03:45) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 8
# of SNPs: 51, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:03:46
1, SNP: 13, Loss: 391.274, OOB Acc: 41.67%, # of Haplo: 17
2, SNP: 2, Loss: 321.685, OOB Acc: 52.08%, # of Haplo: 18
3, SNP: 36, Loss: 232.846, OOB Acc: 58.33%, # of Haplo: 19
4, SNP: 28, Loss: 178.077, OOB Acc: 62.50%, # of Haplo: 20
5, SNP: 35, Loss: 107.151, OOB Acc: 68.75%, # of Haplo: 20
6, SNP: 3, Loss: 72.2736, OOB Acc: 72.92%, # of Haplo: 23
7, SNP: 19, Loss: 50.8439, OOB Acc: 77.08%, # of Haplo: 25
8, SNP: 4, Loss: 47.2744, OOB Acc: 83.33%, # of Haplo: 29
9, SNP: 10, Loss: 40.6216, OOB Acc: 83.33%, # of Haplo: 29
10, SNP: 37, Loss: 38.9199, OOB Acc: 83.33%, # of Haplo: 30
11, SNP: 26, Loss: 32.7795, OOB Acc: 83.33%, # of Haplo: 38
12, SNP: 5, Loss: 31.6837, OOB Acc: 83.33%, # of Haplo: 44
13, SNP: 29, Loss: 31.6837, OOB Acc: 85.42%, # of Haplo: 44
[1] 2019-04-13 03:03:46, OOB Acc: 85.42%, # of SNPs: 13, # of Haplo: 44
1, SNP: 18, Loss: 467.346, OOB Acc: 42.50%, # of Haplo: 17
2, SNP: 51, Loss: 397.716, OOB Acc: 50.00%, # of Haplo: 18
3, SNP: 4, Loss: 286.978, OOB Acc: 55.00%, # of Haplo: 18
4, SNP: 50, Loss: 224.305, OOB Acc: 62.50%, # of Haplo: 19
5, SNP: 34, Loss: 147.479, OOB Acc: 70.00%, # of Haplo: 19
6, SNP: 29, Loss: 139.44, OOB Acc: 75.00%, # of Haplo: 19
7, SNP: 20, Loss: 74.4196, OOB Acc: 80.00%, # of Haplo: 20
8, SNP: 49, Loss: 72.6051, OOB Acc: 82.50%, # of Haplo: 20
9, SNP: 1, Loss: 41.7053, OOB Acc: 85.00%, # of Haplo: 20
10, SNP: 32, Loss: 38.8112, OOB Acc: 85.00%, # of Haplo: 20
11, SNP: 7, Loss: 43.6126, OOB Acc: 87.50%, # of Haplo: 21
12, SNP: 36, Loss: 44.1728, OOB Acc: 92.50%, # of Haplo: 26
13, SNP: 37, Loss: 43.1334, OOB Acc: 92.50%, # of Haplo: 27
14, SNP: 11, Loss: 41.7533, OOB Acc: 92.50%, # of Haplo: 25
[2] 2019-04-13 03:03:46, OOB Acc: 92.50%, # of SNPs: 14, # of Haplo: 25
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.170686e-05 2.247847e-05 2.942300e-05 4.618178e-03 1.769730e-02 5.457802e-02
Max. Mean SD
1.595681e-01 3.334436e-02 3.729916e-02
Accuracy with training data: 93.3%
Out-of-bag accuracy: 89.0%
Gene: C
Training dataset: 60 samples X 51 SNPs
# of HLA alleles: 17
# of individual classifiers: 2
total # of SNPs used: 23
avg. # of SNPs in an individual classifier: 13.50
(sd: 0.71, min: 13, max: 14, median: 13.50)
avg. # of haplotypes in an individual classifier: 34.50
(sd: 13.44, min: 25, max: 44, median: 34.50)
avg. out-of-bag accuracy: 88.96%
(sd: 5.01%, min: 85.42%, max: 92.50%, median: 88.96%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.170686e-05 2.247847e-05 2.942300e-05 4.618178e-03 1.769730e-02 5.457802e-02
Max. Mean SD
1.595681e-01 3.334436e-02 3.729916e-02
Genome assembly: hg19
Gene: C
Training dataset: 60 samples X 51 SNPs
# of HLA alleles: 17
# of individual classifiers: 1
total # of SNPs used: 13
avg. # of SNPs in an individual classifier: 13.00
(sd: NA, min: 13, max: 13, median: 13.00)
avg. # of haplotypes in an individual classifier: 44.00
(sd: NA, min: 44, max: 44, median: 44.00)
avg. out-of-bag accuracy: 85.42%
(sd: NA%, min: 85.42%, max: 85.42%, median: 85.42%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.170686e-05 2.247847e-05 2.942300e-05 4.618178e-03 1.769730e-02 5.457802e-02
Max. Mean SD
1.595681e-01 3.334436e-02 3.729916e-02
Genome assembly: hg19
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:03:46
1, SNP: 44, Loss: 396.506, OOB Acc: 41.67%, # of Haplo: 17
2, SNP: 58, Loss: 291.148, OOB Acc: 50.00%, # of Haplo: 17
3, SNP: 80, Loss: 211.469, OOB Acc: 56.25%, # of Haplo: 20
4, SNP: 18, Loss: 138.615, OOB Acc: 60.42%, # of Haplo: 20
5, SNP: 29, Loss: 111.977, OOB Acc: 62.50%, # of Haplo: 22
6, SNP: 62, Loss: 90.976, OOB Acc: 68.75%, # of Haplo: 24
7, SNP: 13, Loss: 70.2962, OOB Acc: 72.92%, # of Haplo: 24
8, SNP: 14, Loss: 54.5685, OOB Acc: 77.08%, # of Haplo: 22
9, SNP: 72, Loss: 35.1951, OOB Acc: 77.08%, # of Haplo: 24
10, SNP: 3, Loss: 23.2868, OOB Acc: 79.17%, # of Haplo: 24
11, SNP: 70, Loss: 21.089, OOB Acc: 79.17%, # of Haplo: 28
12, SNP: 5, Loss: 20.9664, OOB Acc: 79.17%, # of Haplo: 29
13, SNP: 40, Loss: 20.9662, OOB Acc: 83.33%, # of Haplo: 37
14, SNP: 24, Loss: 20.2385, OOB Acc: 85.42%, # of Haplo: 38
15, SNP: 2, Loss: 20.2383, OOB Acc: 87.50%, # of Haplo: 39
16, SNP: 74, Loss: 20.0601, OOB Acc: 87.50%, # of Haplo: 40
17, SNP: 57, Loss: 17.8846, OOB Acc: 87.50%, # of Haplo: 41
18, SNP: 56, Loss: 14.7377, OOB Acc: 89.58%, # of Haplo: 43
19, SNP: 27, Loss: 10.7709, OOB Acc: 89.58%, # of Haplo: 43
[1] 2019-04-13 03:03:46, OOB Acc: 89.58%, # of SNPs: 19, # of Haplo: 43
1, SNP: 66, Loss: 434.369, OOB Acc: 44.12%, # of Haplo: 19
2, SNP: 28, Loss: 337.451, OOB Acc: 58.82%, # of Haplo: 21
3, SNP: 30, Loss: 302.194, OOB Acc: 73.53%, # of Haplo: 21
4, SNP: 59, Loss: 209.932, OOB Acc: 76.47%, # of Haplo: 21
5, SNP: 74, Loss: 153.579, OOB Acc: 82.35%, # of Haplo: 22
6, SNP: 69, Loss: 108.679, OOB Acc: 88.24%, # of Haplo: 22
7, SNP: 72, Loss: 81.3125, OOB Acc: 91.18%, # of Haplo: 22
8, SNP: 24, Loss: 67.8072, OOB Acc: 91.18%, # of Haplo: 22
9, SNP: 70, Loss: 56.7061, OOB Acc: 91.18%, # of Haplo: 22
10, SNP: 82, Loss: 55.1169, OOB Acc: 94.12%, # of Haplo: 26
11, SNP: 3, Loss: 53.5756, OOB Acc: 94.12%, # of Haplo: 27
12, SNP: 2, Loss: 45.9376, OOB Acc: 94.12%, # of Haplo: 29
13, SNP: 27, Loss: 26.806, OOB Acc: 94.12%, # of Haplo: 29
14, SNP: 5, Loss: 25.3407, OOB Acc: 94.12%, # of Haplo: 30
15, SNP: 32, Loss: 22.6034, OOB Acc: 94.12%, # of Haplo: 31
16, SNP: 78, Loss: 21.7748, OOB Acc: 94.12%, # of Haplo: 37
[2] 2019-04-13 03:03:46, OOB Acc: 94.12%, # of SNPs: 16, # of Haplo: 37
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.944655e-08 6.164670e-06 6.147168e-05 1.555809e-03 6.481833e-03 3.204650e-02
Max. Mean SD
7.898042e-02 1.807739e-02 2.114254e-02
Accuracy with training data: 96.7%
Out-of-bag accuracy: 91.9%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:46
1, SNP: 123, Loss: 203.122, OOB Acc: 54.55%, # of Haplo: 14
2, SNP: 182, Loss: 155.176, OOB Acc: 63.64%, # of Haplo: 16
3, SNP: 204, Loss: 130.453, OOB Acc: 72.73%, # of Haplo: 16
4, SNP: 87, Loss: 81.1116, OOB Acc: 77.27%, # of Haplo: 20
5, SNP: 115, Loss: 60.8965, OOB Acc: 86.36%, # of Haplo: 21
6, SNP: 191, Loss: 35.3274, OOB Acc: 90.91%, # of Haplo: 22
7, SNP: 220, Loss: 23.2514, OOB Acc: 90.91%, # of Haplo: 24
8, SNP: 47, Loss: 19.9968, OOB Acc: 90.91%, # of Haplo: 25
9, SNP: 20, Loss: 18.0597, OOB Acc: 90.91%, # of Haplo: 27
10, SNP: 171, Loss: 17.3174, OOB Acc: 90.91%, # of Haplo: 27
11, SNP: 158, Loss: 16.9514, OOB Acc: 90.91%, # of Haplo: 29
12, SNP: 247, Loss: 15.5658, OOB Acc: 90.91%, # of Haplo: 35
13, SNP: 193, Loss: 15.4994, OOB Acc: 90.91%, # of Haplo: 44
14, SNP: 73, Loss: 12.4986, OOB Acc: 90.91%, # of Haplo: 45
15, SNP: 199, Loss: 11.7779, OOB Acc: 90.91%, # of Haplo: 45
16, SNP: 151, Loss: 11.0487, OOB Acc: 90.91%, # of Haplo: 45
[1] 2019-04-13 03:03:46, OOB Acc: 90.91%, # of SNPs: 16, # of Haplo: 45
1, SNP: 171, Loss: 169.639, OOB Acc: 56.67%, # of Haplo: 14
2, SNP: 127, Loss: 146.326, OOB Acc: 63.33%, # of Haplo: 16
3, SNP: 109, Loss: 104.178, OOB Acc: 66.67%, # of Haplo: 18
4, SNP: 107, Loss: 90.6768, OOB Acc: 73.33%, # of Haplo: 20
5, SNP: 163, Loss: 66.9122, OOB Acc: 76.67%, # of Haplo: 25
6, SNP: 185, Loss: 60.1057, OOB Acc: 76.67%, # of Haplo: 33
7, SNP: 99, Loss: 55.2312, OOB Acc: 76.67%, # of Haplo: 39
8, SNP: 199, Loss: 47.686, OOB Acc: 76.67%, # of Haplo: 39
9, SNP: 95, Loss: 46.9138, OOB Acc: 80.00%, # of Haplo: 47
10, SNP: 70, Loss: 45.9845, OOB Acc: 80.00%, # of Haplo: 49
11, SNP: 129, Loss: 45.86, OOB Acc: 83.33%, # of Haplo: 49
[2] 2019-04-13 03:03:46, OOB Acc: 83.33%, # of SNPs: 11, # of Haplo: 49
1, SNP: 122, Loss: 162.121, OOB Acc: 50.00%, # of Haplo: 12
2, SNP: 137, Loss: 155.367, OOB Acc: 58.33%, # of Haplo: 12
3, SNP: 81, Loss: 120.826, OOB Acc: 58.33%, # of Haplo: 16
4, SNP: 146, Loss: 83.2015, OOB Acc: 62.50%, # of Haplo: 21
5, SNP: 148, Loss: 68.8368, OOB Acc: 62.50%, # of Haplo: 21
6, SNP: 51, Loss: 40.3043, OOB Acc: 62.50%, # of Haplo: 21
7, SNP: 231, Loss: 29.1968, OOB Acc: 62.50%, # of Haplo: 23
8, SNP: 87, Loss: 23.3916, OOB Acc: 62.50%, # of Haplo: 31
9, SNP: 160, Loss: 20.422, OOB Acc: 62.50%, # of Haplo: 31
10, SNP: 119, Loss: 20.2055, OOB Acc: 62.50%, # of Haplo: 32
11, SNP: 9, Loss: 19.9994, OOB Acc: 62.50%, # of Haplo: 37
12, SNP: 199, Loss: 19.2624, OOB Acc: 62.50%, # of Haplo: 38
13, SNP: 136, Loss: 19.0415, OOB Acc: 62.50%, # of Haplo: 40
14, SNP: 151, Loss: 18.6147, OOB Acc: 62.50%, # of Haplo: 40
15, SNP: 3, Loss: 18.5586, OOB Acc: 62.50%, # of Haplo: 45
16, SNP: 73, Loss: 14.2585, OOB Acc: 62.50%, # of Haplo: 46
[3] 2019-04-13 03:03:46, OOB Acc: 62.50%, # of SNPs: 16, # of Haplo: 46
1, SNP: 122, Loss: 185.298, OOB Acc: 75.00%, # of Haplo: 13
2, SNP: 118, Loss: 178.626, OOB Acc: 83.33%, # of Haplo: 13
3, SNP: 131, Loss: 149.969, OOB Acc: 87.50%, # of Haplo: 14
4, SNP: 144, Loss: 97.8546, OOB Acc: 87.50%, # of Haplo: 14
5, SNP: 123, Loss: 62.1051, OOB Acc: 91.67%, # of Haplo: 14
6, SNP: 105, Loss: 48.1095, OOB Acc: 95.83%, # of Haplo: 14
7, SNP: 197, Loss: 30.2151, OOB Acc: 95.83%, # of Haplo: 16
8, SNP: 40, Loss: 28.3663, OOB Acc: 95.83%, # of Haplo: 20
9, SNP: 90, Loss: 24.3188, OOB Acc: 95.83%, # of Haplo: 22
10, SNP: 263, Loss: 19.7509, OOB Acc: 95.83%, # of Haplo: 31
11, SNP: 67, Loss: 15.2521, OOB Acc: 95.83%, # of Haplo: 31
12, SNP: 199, Loss: 14.3355, OOB Acc: 95.83%, # of Haplo: 33
13, SNP: 73, Loss: 9.42737, OOB Acc: 95.83%, # of Haplo: 33
[4] 2019-04-13 03:03:46, OOB Acc: 95.83%, # of SNPs: 13, # of Haplo: 33
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.110187e-05 8.734649e-05 1.435481e-04 3.012736e-03 6.600797e-03 4.079281e-02
Max. Mean SD
4.833812e-01 4.385633e-02 1.130754e-01
Accuracy with training data: 97.1%
Out-of-bag accuracy: 83.1%
Gene: A
Training dataset: 34 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 46
avg. # of SNPs in an individual classifier: 14.00
(sd: 2.45, min: 11, max: 16, median: 14.50)
avg. # of haplotypes in an individual classifier: 43.25
(sd: 7.04, min: 33, max: 49, median: 45.50)
avg. out-of-bag accuracy: 83.14%
(sd: 14.69%, min: 62.50%, max: 95.83%, median: 87.12%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.110187e-05 8.734649e-05 1.435481e-04 3.012736e-03 6.600797e-03 4.079281e-02
Max. Mean SD
4.833812e-01 4.385633e-02 1.130754e-01
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:46) 0%
Predicting (2019-04-13 03:03:46) 100%
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:03:46
1, SNP: 44, Loss: 396.506, OOB Acc: 41.67%, # of Haplo: 17
2, SNP: 58, Loss: 291.148, OOB Acc: 50.00%, # of Haplo: 17
3, SNP: 80, Loss: 211.469, OOB Acc: 56.25%, # of Haplo: 20
4, SNP: 18, Loss: 138.615, OOB Acc: 60.42%, # of Haplo: 20
5, SNP: 29, Loss: 111.977, OOB Acc: 62.50%, # of Haplo: 22
6, SNP: 62, Loss: 90.976, OOB Acc: 68.75%, # of Haplo: 24
7, SNP: 13, Loss: 70.2962, OOB Acc: 72.92%, # of Haplo: 24
8, SNP: 14, Loss: 54.5685, OOB Acc: 77.08%, # of Haplo: 22
9, SNP: 72, Loss: 35.1951, OOB Acc: 77.08%, # of Haplo: 24
10, SNP: 3, Loss: 23.2868, OOB Acc: 79.17%, # of Haplo: 24
11, SNP: 70, Loss: 21.089, OOB Acc: 79.17%, # of Haplo: 28
12, SNP: 5, Loss: 20.9664, OOB Acc: 79.17%, # of Haplo: 29
13, SNP: 40, Loss: 20.9662, OOB Acc: 83.33%, # of Haplo: 37
14, SNP: 24, Loss: 20.2385, OOB Acc: 85.42%, # of Haplo: 38
15, SNP: 2, Loss: 20.2383, OOB Acc: 87.50%, # of Haplo: 39
16, SNP: 74, Loss: 20.0601, OOB Acc: 87.50%, # of Haplo: 40
17, SNP: 57, Loss: 17.8846, OOB Acc: 87.50%, # of Haplo: 41
18, SNP: 56, Loss: 14.7377, OOB Acc: 89.58%, # of Haplo: 43
19, SNP: 27, Loss: 10.7709, OOB Acc: 89.58%, # of Haplo: 43
[1] 2019-04-13 03:03:46, OOB Acc: 89.58%, # of SNPs: 19, # of Haplo: 43
1, SNP: 66, Loss: 434.369, OOB Acc: 44.12%, # of Haplo: 19
2, SNP: 28, Loss: 337.451, OOB Acc: 58.82%, # of Haplo: 21
3, SNP: 30, Loss: 302.194, OOB Acc: 73.53%, # of Haplo: 21
4, SNP: 59, Loss: 209.932, OOB Acc: 76.47%, # of Haplo: 21
5, SNP: 74, Loss: 153.579, OOB Acc: 82.35%, # of Haplo: 22
6, SNP: 69, Loss: 108.679, OOB Acc: 88.24%, # of Haplo: 22
7, SNP: 72, Loss: 81.3125, OOB Acc: 91.18%, # of Haplo: 22
8, SNP: 24, Loss: 67.8072, OOB Acc: 91.18%, # of Haplo: 22
9, SNP: 70, Loss: 56.7061, OOB Acc: 91.18%, # of Haplo: 22
10, SNP: 82, Loss: 55.1169, OOB Acc: 94.12%, # of Haplo: 26
11, SNP: 3, Loss: 53.5756, OOB Acc: 94.12%, # of Haplo: 27
12, SNP: 2, Loss: 45.9376, OOB Acc: 94.12%, # of Haplo: 29
13, SNP: 27, Loss: 26.806, OOB Acc: 94.12%, # of Haplo: 29
14, SNP: 5, Loss: 25.3407, OOB Acc: 94.12%, # of Haplo: 30
15, SNP: 32, Loss: 22.6034, OOB Acc: 94.12%, # of Haplo: 31
16, SNP: 78, Loss: 21.7748, OOB Acc: 94.12%, # of Haplo: 37
[2] 2019-04-13 03:03:46, OOB Acc: 94.12%, # of SNPs: 16, # of Haplo: 37
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.944655e-08 6.164670e-06 6.147168e-05 1.555809e-03 6.481833e-03 3.204650e-02
Max. Mean SD
7.898042e-02 1.807739e-02 2.114254e-02
Accuracy with training data: 96.7%
Out-of-bag accuracy: 91.9%
Gene: C
Training dataset: 60 samples X 83 SNPs
# of HLA alleles: 17
# of individual classifiers: 2
total # of SNPs used: 27
avg. # of SNPs in an individual classifier: 17.50
(sd: 2.12, min: 16, max: 19, median: 17.50)
avg. # of haplotypes in an individual classifier: 40.00
(sd: 4.24, min: 37, max: 43, median: 40.00)
avg. out-of-bag accuracy: 91.85%
(sd: 3.21%, min: 89.58%, max: 94.12%, median: 91.85%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.944655e-08 6.164670e-06 6.147168e-05 1.555809e-03 6.481833e-03 3.204650e-02
Max. Mean SD
7.898042e-02 1.807739e-02 2.114254e-02
Genome assembly: hg19
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
>
> proc.time()
user system elapsed
27.25 0.45 29.48
|
HIBAG.Rcheck/tests_x64/runTests.Rout
R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> #############################################################
> #
> # DESCRIPTION: Unit tests in the HIBAG package
> #
>
> # load the HIBAG package
> library(HIBAG)
HIBAG (HLA Genotype Imputation with Attribute Bagging)
Kernel Version: v1.4
Supported by Streaming SIMD Extensions (SSE2)
>
>
> #############################################################
>
> # a list of HLA genes
> hla.list <- c("A", "B", "C", "DQA1", "DQB1", "DRB1")
>
> # pre-defined lower bound of prediction accuracy
> hla.acc <- c(0.9, 0.8, 0.8, 0.85, 0.85, 0.75)
>
>
> for (hla.idx in seq_len(length(hla.list)))
+ {
+ hla.id <- hla.list[hla.idx]
+
+ # make a "hlaAlleleClass" object
+ hla <- hlaAllele(HLA_Type_Table$sample.id,
+ H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")],
+ H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")],
+ locus=hla.id, assembly="hg19")
+
+ # divide HLA types randomly
+ set.seed(100)
+ hlatab <- hlaSplitAllele(hla, train.prop=0.5)
+
+ # SNP predictors within the flanking region on each side
+ region <- 500 # kb
+ snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id,
+ HapMap_CEU_Geno$snp.position,
+ hla.id, region*1000, assembly="hg19")
+
+ # training and validation genotypes
+ train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
+ snp.sel=match(snpid, HapMap_CEU_Geno$snp.id),
+ samp.sel=match(hlatab$training$value$sample.id,
+ HapMap_CEU_Geno$sample.id))
+ test.geno <- hlaGenoSubset(HapMap_CEU_Geno,
+ samp.sel=match(hlatab$validation$value$sample.id,
+ HapMap_CEU_Geno$sample.id))
+
+
+ # train a HIBAG model
+ set.seed(100)
+ model <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=10)
+ summary(model)
+
+ # validation
+ pred <- predict(model, test.geno)
+ summary(pred)
+
+ # compare
+ comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
+ call.threshold=0)
+ print(comp$overall)
+
+ # check
+ if (comp$overall$acc.haplo < hla.acc[hla.idx])
+ {
+ stop("HLA - ", hla.id, ", 'acc.haplo' should be >= ",
+ hla.acc[hla.idx], ".")
+ }
+
+ cat("\n\n")
+ }
Exclude 9 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:47
[1] 2019-04-13 03:03:47, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 42
[2] 2019-04-13 03:03:47, OOB Acc: 92.31%, # of SNPs: 20, # of Haplo: 48
[3] 2019-04-13 03:03:47, OOB Acc: 91.18%, # of SNPs: 18, # of Haplo: 45
[4] 2019-04-13 03:03:47, OOB Acc: 90.91%, # of SNPs: 16, # of Haplo: 39
[5] 2019-04-13 03:03:47, OOB Acc: 83.33%, # of SNPs: 13, # of Haplo: 31
[6] 2019-04-13 03:03:47, OOB Acc: 94.44%, # of SNPs: 15, # of Haplo: 27
[7] 2019-04-13 03:03:48, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 56
[8] 2019-04-13 03:03:48, OOB Acc: 81.82%, # of SNPs: 17, # of Haplo: 36
[9] 2019-04-13 03:03:48, OOB Acc: 90.91%, # of SNPs: 18, # of Haplo: 75
[10] 2019-04-13 03:03:48, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 55
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004866021 0.0004930254 0.0005508348 0.0035299669 0.0069578428 0.0182374201
Max. Mean SD
0.3535622480 0.0343318466 0.0826524583
Accuracy with training data: 98.5%
Out-of-bag accuracy: 89.4%
Gene: A
Training dataset: 34 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 10
total # of SNPs used: 104
avg. # of SNPs in an individual classifier: 16.20
(sd: 2.04, min: 13, max: 20, median: 15.50)
avg. # of haplotypes in an individual classifier: 45.40
(sd: 14.04, min: 27, max: 75, median: 43.50)
avg. out-of-bag accuracy: 89.42%
(sd: 4.00%, min: 81.82%, max: 94.44%, median: 90.91%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004866021 0.0004930254 0.0005508348 0.0035299669 0.0069578428 0.0182374201
Max. Mean SD
0.3535622480 0.0343318466 0.0826524583
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:48) 0%
Predicting (2019-04-13 03:03:48) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 2 (7.7%) 7 (26.9%) 16 (61.5%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.000448 0.006274 0.022911 0.021609 0.353562
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 26 24 50 0.9230769 0.9615385 0
n.call call.rate
1 26 1
Exclude 1 monomorphic SNP
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 340, # of samples: 28
# of unique HLA alleles: 22
[-] 2019-04-13 03:03:48
[1] 2019-04-13 03:03:49, OOB Acc: 75.00%, # of SNPs: 12, # of Haplo: 121
[2] 2019-04-13 03:03:49, OOB Acc: 60.00%, # of SNPs: 17, # of Haplo: 64
[3] 2019-04-13 03:03:49, OOB Acc: 63.64%, # of SNPs: 18, # of Haplo: 158
[4] 2019-04-13 03:03:49, OOB Acc: 72.73%, # of SNPs: 12, # of Haplo: 49
[5] 2019-04-13 03:03:49, OOB Acc: 72.73%, # of SNPs: 12, # of Haplo: 33
[6] 2019-04-13 03:03:50, OOB Acc: 77.78%, # of SNPs: 17, # of Haplo: 89
[7] 2019-04-13 03:03:50, OOB Acc: 63.64%, # of SNPs: 23, # of Haplo: 121
[8] 2019-04-13 03:03:50, OOB Acc: 50.00%, # of SNPs: 25, # of Haplo: 95
[9] 2019-04-13 03:03:50, OOB Acc: 71.43%, # of SNPs: 18, # of Haplo: 58
[10] 2019-04-13 03:03:51, OOB Acc: 72.22%, # of SNPs: 17, # of Haplo: 99
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
6.377942e-05 8.530385e-05 2.790238e-04 2.040836e-03 4.488214e-03 1.052265e-02
Max. Mean SD
1.458034e-01 1.506772e-02 3.127080e-02
Accuracy with training data: 98.2%
Out-of-bag accuracy: 67.9%
Gene: B
Training dataset: 28 samples X 340 SNPs
# of HLA alleles: 22
# of individual classifiers: 10
total # of SNPs used: 115
avg. # of SNPs in an individual classifier: 17.10
(sd: 4.43, min: 12, max: 25, median: 17.00)
avg. # of haplotypes in an individual classifier: 88.70
(sd: 38.39, min: 33, max: 158, median: 92.00)
avg. out-of-bag accuracy: 67.92%
(sd: 8.46%, min: 50.00%, max: 77.78%, median: 71.83%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
6.377942e-05 8.530385e-05 2.790238e-04 2.040836e-03 4.488214e-03 1.052265e-02
Max. Mean SD
1.458034e-01 1.506772e-02 3.127080e-02
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 340 SNPs, 22 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 15
Predicting (2019-04-13 03:03:51) 0%
Predicting (2019-04-13 03:03:51) 100%
Gene: B
Range: [31321649bp, 31324989bp] on hg19
# of samples: 15
# of unique HLA alleles: 11
# of unique HLA genotypes: 13
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
3 (20.0%) 4 (26.7%) 4 (26.7%) 4 (26.7%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
2.000e-08 2.950e-04 7.655e-04 1.920e-02 1.067e-02 2.065e-01
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 15 11 25 0.7333333 0.8333333 0
n.call call.rate
1 15 1
Exclude 2 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 354, # of samples: 36
# of unique HLA alleles: 17
[-] 2019-04-13 03:03:51
[1] 2019-04-13 03:03:51, OOB Acc: 96.15%, # of SNPs: 19, # of Haplo: 35
[2] 2019-04-13 03:03:51, OOB Acc: 90.91%, # of SNPs: 25, # of Haplo: 75
[3] 2019-04-13 03:03:51, OOB Acc: 66.67%, # of SNPs: 14, # of Haplo: 69
[4] 2019-04-13 03:03:51, OOB Acc: 76.92%, # of SNPs: 16, # of Haplo: 38
[5] 2019-04-13 03:03:52, OOB Acc: 79.17%, # of SNPs: 25, # of Haplo: 64
[6] 2019-04-13 03:03:52, OOB Acc: 91.67%, # of SNPs: 36, # of Haplo: 52
[7] 2019-04-13 03:03:52, OOB Acc: 92.31%, # of SNPs: 16, # of Haplo: 58
[8] 2019-04-13 03:03:52, OOB Acc: 89.29%, # of SNPs: 19, # of Haplo: 26
[9] 2019-04-13 03:03:52, OOB Acc: 91.67%, # of SNPs: 21, # of Haplo: 71
[10] 2019-04-13 03:03:53, OOB Acc: 81.25%, # of SNPs: 25, # of Haplo: 77
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0003038237 0.0003220623 0.0004862091 0.0018325695 0.0037925315 0.0087410133
Max. Mean SD
0.0940510882 0.0104030527 0.0181801883
Accuracy with training data: 100.0%
Out-of-bag accuracy: 85.6%
Gene: C
Training dataset: 36 samples X 354 SNPs
# of HLA alleles: 17
# of individual classifiers: 10
total # of SNPs used: 146
avg. # of SNPs in an individual classifier: 21.60
(sd: 6.47, min: 14, max: 36, median: 20.00)
avg. # of haplotypes in an individual classifier: 56.50
(sd: 18.08, min: 26, max: 77, median: 61.00)
avg. out-of-bag accuracy: 85.60%
(sd: 9.22%, min: 66.67%, max: 96.15%, median: 90.10%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0003038237 0.0003220623 0.0004862091 0.0018325695 0.0037925315 0.0087410133
Max. Mean SD
0.0940510882 0.0104030527 0.0181801883
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 354 SNPs, 17 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 24
Predicting (2019-04-13 03:03:53) 0%
Predicting (2019-04-13 03:03:53) 100%
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 24
# of unique HLA alleles: 14
# of unique HLA genotypes: 16
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
4 (16.7%) 3 (12.5%) 6 (25.0%) 11 (45.8%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000e+00 7.431e-05 5.956e-04 4.933e-03 4.212e-03 3.363e-02
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 24 18 42 0.75 0.875 0
n.call call.rate
1 24 1
Exclude 6 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 343, # of samples: 31
# of unique HLA alleles: 7
[-] 2019-04-13 03:03:53
[1] 2019-04-13 03:03:53, OOB Acc: 86.36%, # of SNPs: 10, # of Haplo: 19
[2] 2019-04-13 03:03:53, OOB Acc: 92.31%, # of SNPs: 12, # of Haplo: 37
[3] 2019-04-13 03:03:53, OOB Acc: 86.36%, # of SNPs: 12, # of Haplo: 21
[4] 2019-04-13 03:03:53, OOB Acc: 95.00%, # of SNPs: 10, # of Haplo: 17
[5] 2019-04-13 03:03:53, OOB Acc: 88.89%, # of SNPs: 11, # of Haplo: 31
[6] 2019-04-13 03:03:53, OOB Acc: 100.00%, # of SNPs: 14, # of Haplo: 41
[7] 2019-04-13 03:03:53, OOB Acc: 90.00%, # of SNPs: 9, # of Haplo: 16
[8] 2019-04-13 03:03:53, OOB Acc: 95.45%, # of SNPs: 10, # of Haplo: 10
[9] 2019-04-13 03:03:53, OOB Acc: 83.33%, # of SNPs: 7, # of Haplo: 9
[10] 2019-04-13 03:03:53, OOB Acc: 87.50%, # of SNPs: 7, # of Haplo: 17
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.002003247 0.002016202 0.002132800 0.009820132 0.018803876 0.040262061
Max. Mean SD
0.496473774 0.061923149 0.128202241
Accuracy with training data: 96.8%
Out-of-bag accuracy: 90.5%
Gene: DQA1
Training dataset: 31 samples X 343 SNPs
# of HLA alleles: 7
# of individual classifiers: 10
total # of SNPs used: 73
avg. # of SNPs in an individual classifier: 10.20
(sd: 2.20, min: 7, max: 14, median: 10.00)
avg. # of haplotypes in an individual classifier: 21.80
(sd: 10.93, min: 9, max: 41, median: 18.00)
avg. out-of-bag accuracy: 90.52%
(sd: 5.12%, min: 83.33%, max: 100.00%, median: 89.44%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.002003247 0.002016202 0.002132800 0.009820132 0.018803876 0.040262061
Max. Mean SD
0.496473774 0.061923149 0.128202241
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 343 SNPs, 7 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 29
Predicting (2019-04-13 03:03:53) 0%
Predicting (2019-04-13 03:03:53) 100%
Gene: DQA1
Range: [32605169bp, 32612152bp] on hg19
# of samples: 29
# of unique HLA alleles: 7
# of unique HLA genotypes: 16
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
3 (10.3%) 5 (17.2%) 5 (17.2%) 16 (55.2%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0007125 0.0034787 0.0080790 0.0290830 0.0159372 0.4975405
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 29 23 51 0.7931034 0.8793103 0
n.call call.rate
1 29 1
Exclude 5 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 351, # of samples: 34
# of unique HLA alleles: 12
[-] 2019-04-13 03:03:53
[1] 2019-04-13 03:03:53, OOB Acc: 81.82%, # of SNPs: 17, # of Haplo: 58
[2] 2019-04-13 03:03:53, OOB Acc: 80.77%, # of SNPs: 14, # of Haplo: 42
[3] 2019-04-13 03:03:53, OOB Acc: 76.67%, # of SNPs: 11, # of Haplo: 23
[4] 2019-04-13 03:03:53, OOB Acc: 78.57%, # of SNPs: 14, # of Haplo: 16
[5] 2019-04-13 03:03:53, OOB Acc: 89.29%, # of SNPs: 16, # of Haplo: 37
[6] 2019-04-13 03:03:54, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 40
[7] 2019-04-13 03:03:54, OOB Acc: 88.46%, # of SNPs: 14, # of Haplo: 26
[8] 2019-04-13 03:03:54, OOB Acc: 92.31%, # of SNPs: 16, # of Haplo: 35
[9] 2019-04-13 03:03:54, OOB Acc: 87.50%, # of SNPs: 11, # of Haplo: 70
[10] 2019-04-13 03:03:54, OOB Acc: 80.00%, # of SNPs: 10, # of Haplo: 27
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004974378 0.0005193302 0.0007163617 0.0045545694 0.0089462549 0.0282489093
Max. Mean SD
0.5204994077 0.0604813618 0.1368041942
Accuracy with training data: 94.1%
Out-of-bag accuracy: 84.6%
Gene: DQB1
Training dataset: 34 samples X 351 SNPs
# of HLA alleles: 12
# of individual classifiers: 10
total # of SNPs used: 89
avg. # of SNPs in an individual classifier: 13.70
(sd: 2.36, min: 10, max: 17, median: 14.00)
avg. # of haplotypes in an individual classifier: 37.40
(sd: 16.41, min: 16, max: 70, median: 36.00)
avg. out-of-bag accuracy: 84.63%
(sd: 5.65%, min: 76.67%, max: 92.31%, median: 84.66%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004974378 0.0005193302 0.0007163617 0.0045545694 0.0089462549 0.0282489093
Max. Mean SD
0.5204994077 0.0604813618 0.1368041942
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 351 SNPs, 12 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:54) 0%
Predicting (2019-04-13 03:03:54) 100%
Gene: DQB1
Range: [32627241bp, 32634466bp] on hg19
# of samples: 26
# of unique HLA alleles: 10
# of unique HLA genotypes: 16
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 5 (19.2%) 6 (23.1%) 14 (53.8%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
2.100e-08 7.787e-04 4.277e-03 7.451e-03 1.142e-02 2.731e-02
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 26 22 46 0.8461538 0.8846154 0
n.call call.rate
1 26 1
Exclude 4 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 18
# of SNPs: 323, # of samples: 35
# of unique HLA alleles: 20
[-] 2019-04-13 03:03:54
[1] 2019-04-13 03:03:54, OOB Acc: 70.00%, # of SNPs: 19, # of Haplo: 83
[2] 2019-04-13 03:03:54, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 59
[3] 2019-04-13 03:03:55, OOB Acc: 79.17%, # of SNPs: 14, # of Haplo: 68
[4] 2019-04-13 03:03:55, OOB Acc: 86.36%, # of SNPs: 18, # of Haplo: 57
[5] 2019-04-13 03:03:55, OOB Acc: 75.00%, # of SNPs: 16, # of Haplo: 56
[6] 2019-04-13 03:03:55, OOB Acc: 75.00%, # of SNPs: 19, # of Haplo: 152
[7] 2019-04-13 03:03:56, OOB Acc: 69.23%, # of SNPs: 20, # of Haplo: 129
[8] 2019-04-13 03:03:56, OOB Acc: 71.88%, # of SNPs: 16, # of Haplo: 61
[9] 2019-04-13 03:03:56, OOB Acc: 69.23%, # of SNPs: 24, # of Haplo: 65
[10] 2019-04-13 03:03:56, OOB Acc: 80.00%, # of SNPs: 28, # of Haplo: 117
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0006122513 0.0006129451 0.0006191888 0.0015993856 0.0024553804 0.0054592144
Max. Mean SD
0.5551955860 0.0342140882 0.1257480491
Accuracy with training data: 95.7%
Out-of-bag accuracy: 76.3%
Gene: DRB1
Training dataset: 35 samples X 323 SNPs
# of HLA alleles: 20
# of individual classifiers: 10
total # of SNPs used: 137
avg. # of SNPs in an individual classifier: 18.90
(sd: 4.31, min: 14, max: 28, median: 18.50)
avg. # of haplotypes in an individual classifier: 84.70
(sd: 34.99, min: 56, max: 152, median: 66.50)
avg. out-of-bag accuracy: 76.34%
(sd: 6.76%, min: 69.23%, max: 87.50%, median: 75.00%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0006122513 0.0006129451 0.0006191888 0.0015993856 0.0024553804 0.0054592144
Max. Mean SD
0.5551955860 0.0342140882 0.1257480491
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 323 SNPs, 20 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 25
Predicting (2019-04-13 03:03:56) 0%
Predicting (2019-04-13 03:03:56) 100%
Gene: DRB1
Range: [32546546bp, 32557613bp] on hg19
# of samples: 25
# of unique HLA alleles: 9
# of unique HLA genotypes: 17
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
6 (24.0%) 6 (24.0%) 7 (28.0%) 6 (24.0%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0000000 0.0003357 0.0334829 0.0008981 0.5581343
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 25 16 39 0.64 0.78 0
n.call call.rate
1 25 1
>
>
>
> #############################################################
>
> {
+ function.list <- readRDS(
+ system.file("Meta", "Rd.rds", package="HIBAG"))$Name
+
+ sapply(function.list, FUN = function(func.name)
+ {
+ args <- list(
+ topic = func.name,
+ package = "HIBAG",
+ echo = FALSE,
+ verbose = FALSE,
+ ask = FALSE
+ )
+ suppressWarnings(do.call(example, args))
+ NULL
+ })
+ invisible()
+ }
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 24
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:56
1, SNP: 155, Loss: 186.198, OOB Acc: 68.18%, # of Haplo: 13
2, SNP: 111, Loss: 138.902, OOB Acc: 72.73%, # of Haplo: 13
3, SNP: 159, Loss: 134.624, OOB Acc: 81.82%, # of Haplo: 15
4, SNP: 75, Loss: 120.53, OOB Acc: 86.36%, # of Haplo: 16
5, SNP: 129, Loss: 79.3436, OOB Acc: 90.91%, # of Haplo: 20
6, SNP: 135, Loss: 56.5856, OOB Acc: 90.91%, # of Haplo: 20
7, SNP: 162, Loss: 40.1347, OOB Acc: 90.91%, # of Haplo: 20
8, SNP: 195, Loss: 29.7565, OOB Acc: 90.91%, # of Haplo: 24
9, SNP: 182, Loss: 23.1586, OOB Acc: 90.91%, # of Haplo: 24
10, SNP: 24, Loss: 16.7007, OOB Acc: 90.91%, # of Haplo: 27
11, SNP: 154, Loss: 15.6368, OOB Acc: 90.91%, # of Haplo: 28
12, SNP: 17, Loss: 15.4059, OOB Acc: 90.91%, # of Haplo: 39
13, SNP: 199, Loss: 14.4894, OOB Acc: 90.91%, # of Haplo: 40
14, SNP: 73, Loss: 8.03154, OOB Acc: 90.91%, # of Haplo: 42
15, SNP: 151, Loss: 5.75619, OOB Acc: 90.91%, # of Haplo: 42
[1] 2019-04-13 03:03:57, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 42
1, SNP: 87, Loss: 163.788, OOB Acc: 50.00%, # of Haplo: 15
2, SNP: 179, Loss: 159.036, OOB Acc: 57.69%, # of Haplo: 16
3, SNP: 162, Loss: 127.744, OOB Acc: 61.54%, # of Haplo: 17
4, SNP: 113, Loss: 120.348, OOB Acc: 76.92%, # of Haplo: 17
5, SNP: 149, Loss: 99.7027, OOB Acc: 80.77%, # of Haplo: 19
6, SNP: 183, Loss: 63.9201, OOB Acc: 80.77%, # of Haplo: 19
7, SNP: 82, Loss: 46.9454, OOB Acc: 80.77%, # of Haplo: 19
8, SNP: 73, Loss: 36.2707, OOB Acc: 84.62%, # of Haplo: 21
9, SNP: 47, Loss: 34.9876, OOB Acc: 84.62%, # of Haplo: 22
10, SNP: 140, Loss: 27.9494, OOB Acc: 84.62%, # of Haplo: 22
11, SNP: 189, Loss: 23.9141, OOB Acc: 84.62%, # of Haplo: 23
12, SNP: 249, Loss: 18.884, OOB Acc: 84.62%, # of Haplo: 27
13, SNP: 151, Loss: 15.8511, OOB Acc: 84.62%, # of Haplo: 27
14, SNP: 21, Loss: 11.1982, OOB Acc: 88.46%, # of Haplo: 30
15, SNP: 215, Loss: 10.7762, OOB Acc: 88.46%, # of Haplo: 32
16, SNP: 136, Loss: 10.5472, OOB Acc: 88.46%, # of Haplo: 38
17, SNP: 75, Loss: 10.4276, OOB Acc: 88.46%, # of Haplo: 49
18, SNP: 28, Loss: 10.3043, OOB Acc: 88.46%, # of Haplo: 55
19, SNP: 266, Loss: 10.3043, OOB Acc: 92.31%, # of Haplo: 48
20, SNP: 199, Loss: 9.5327, OOB Acc: 92.31%, # of Haplo: 48
[2] 2019-04-13 03:03:57, OOB Acc: 92.31%, # of SNPs: 20, # of Haplo: 48
1, SNP: 167, Loss: 182.217, OOB Acc: 55.88%, # of Haplo: 14
2, SNP: 126, Loss: 120.313, OOB Acc: 64.71%, # of Haplo: 16
3, SNP: 135, Loss: 94.3559, OOB Acc: 73.53%, # of Haplo: 19
4, SNP: 173, Loss: 73.5379, OOB Acc: 79.41%, # of Haplo: 19
5, SNP: 196, Loss: 47.4931, OOB Acc: 85.29%, # of Haplo: 20
6, SNP: 124, Loss: 28.5275, OOB Acc: 88.24%, # of Haplo: 20
7, SNP: 112, Loss: 27.3051, OOB Acc: 88.24%, # of Haplo: 22
8, SNP: 150, Loss: 26.8966, OOB Acc: 88.24%, # of Haplo: 22
9, SNP: 106, Loss: 20.7953, OOB Acc: 88.24%, # of Haplo: 21
10, SNP: 116, Loss: 20.4753, OOB Acc: 88.24%, # of Haplo: 21
11, SNP: 16, Loss: 20.2453, OOB Acc: 88.24%, # of Haplo: 24
12, SNP: 6, Loss: 20.1953, OOB Acc: 88.24%, # of Haplo: 28
13, SNP: 181, Loss: 19.9842, OOB Acc: 88.24%, # of Haplo: 28
14, SNP: 73, Loss: 11.7972, OOB Acc: 91.18%, # of Haplo: 33
15, SNP: 96, Loss: 11.7743, OOB Acc: 91.18%, # of Haplo: 37
16, SNP: 177, Loss: 11.7597, OOB Acc: 91.18%, # of Haplo: 45
17, SNP: 151, Loss: 11.3816, OOB Acc: 91.18%, # of Haplo: 45
18, SNP: 199, Loss: 10.1586, OOB Acc: 91.18%, # of Haplo: 45
[3] 2019-04-13 03:03:57, OOB Acc: 91.18%, # of SNPs: 18, # of Haplo: 45
1, SNP: 191, Loss: 189.049, OOB Acc: 63.64%, # of Haplo: 15
2, SNP: 100, Loss: 160.313, OOB Acc: 72.73%, # of Haplo: 18
3, SNP: 158, Loss: 135.403, OOB Acc: 77.27%, # of Haplo: 19
4, SNP: 86, Loss: 112.795, OOB Acc: 86.36%, # of Haplo: 22
5, SNP: 134, Loss: 75.8819, OOB Acc: 86.36%, # of Haplo: 24
6, SNP: 85, Loss: 54.3124, OOB Acc: 90.91%, # of Haplo: 29
7, SNP: 128, Loss: 36.9547, OOB Acc: 90.91%, # of Haplo: 29
8, SNP: 77, Loss: 30.0981, OOB Acc: 90.91%, # of Haplo: 32
9, SNP: 54, Loss: 24.7805, OOB Acc: 90.91%, # of Haplo: 32
10, SNP: 21, Loss: 20.945, OOB Acc: 90.91%, # of Haplo: 36
11, SNP: 199, Loss: 20.2197, OOB Acc: 90.91%, # of Haplo: 36
12, SNP: 266, Loss: 19.48, OOB Acc: 90.91%, # of Haplo: 38
13, SNP: 52, Loss: 19.1914, OOB Acc: 90.91%, # of Haplo: 38
14, SNP: 215, Loss: 18.9375, OOB Acc: 90.91%, # of Haplo: 38
15, SNP: 73, Loss: 14.4966, OOB Acc: 90.91%, # of Haplo: 38
16, SNP: 151, Loss: 13.3459, OOB Acc: 90.91%, # of Haplo: 39
[4] 2019-04-13 03:03:57, OOB Acc: 90.91%, # of SNPs: 16, # of Haplo: 39
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001216782 0.0001372891 0.0002777870 0.0023012412 0.0072873069 0.0234246874
Max. Mean SD
0.2886695022 0.0296925310 0.0666389121
Accuracy with training data: 98.5%
Out-of-bag accuracy: 91.3%
Gene: A
Training dataset: 34 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 54
avg. # of SNPs in an individual classifier: 17.25
(sd: 2.22, min: 15, max: 20, median: 17.00)
avg. # of haplotypes in an individual classifier: 43.50
(sd: 3.87, min: 39, max: 48, median: 43.50)
avg. out-of-bag accuracy: 91.33%
(sd: 0.67%, min: 90.91%, max: 92.31%, median: 91.04%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001216782 0.0001372891 0.0002777870 0.0023012412 0.0072873069 0.0234246874
Max. Mean SD
0.2886695022 0.0296925310 0.0666389121
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:57) 0%
Predicting (2019-04-13 03:03:57) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 2 (7.7%) 3 (11.5%) 20 (76.9%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0007878 0.0047578 0.0193435 0.0128352 0.2886695
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:57) 0%
Predicting (2019-04-13 03:03:57) 100%
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 90
Predicting (2019-04-13 03:03:57) 0%
Predicting (2019-04-13 03:03:57) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
using the default genome assembly (assembly="hg19")
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 12
# of unique HLA genotypes: 28
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 100
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
dominant model:
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042*
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000
02:01 25 35 52.0 48.6 0.0000 1.000 1.000
02:06 59 1 50.8 0.0 0.0000 1.000 1.000
03:01 51 9 49.0 55.6 0.0000 1.000 1.000
11:01 55 5 50.9 40.0 0.0000 1.000 1.000
23:01 58 2 50.0 50.0 0.0000 1.000 1.000
24:03 59 1 50.8 0.0 0.0000 1.000 1.000
25:01 55 5 52.7 20.0 0.8727 0.350 0.353
26:01 57 3 52.6 0.0 1.4035 0.236 0.237
29:02 56 4 51.8 25.0 0.2679 0.605 0.612
31:01 57 3 49.1 66.7 0.0000 1.000 1.000
32:01 56 4 46.4 100.0 2.4107 0.121 0.112
68:01 57 3 52.6 0.0 1.4035 0.236 0.237
additive model:
[-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p
01:01 95 25 50.5 48.0 0.0000 1.000 1.000
02:01 77 43 48.1 53.5 0.1450 0.703 0.704
02:06 119 1 50.4 0.0 0.0000 1.000 1.000
03:01 111 9 49.5 55.6 0.0000 1.000 1.000
11:01 115 5 50.4 40.0 0.0000 1.000 1.000
23:01 117 3 50.4 33.3 0.0000 1.000 1.000
24:02 109 11 46.8 81.8 3.6030 0.058 0.053
24:03 119 1 50.4 0.0 0.0000 1.000 1.000
25:01 115 5 51.3 20.0 0.8348 0.361 0.364
26:01 117 3 51.3 0.0 1.3675 0.242 0.244
29:02 116 4 50.9 25.0 0.2586 0.611 0.619
31:01 117 3 49.6 66.7 0.0000 1.000 1.000
32:01 116 4 48.3 100.0 2.3276 0.127 0.119
68:01 117 3 51.3 0.0 1.3675 0.242 0.244
recessive model:
[-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p
01:01 59 1 50.8 0 0.000 1.000 1.000
02:01 52 8 46.2 75 1.298 0.255 0.254
02:06 60 0 50.0 . . . .
03:01 60 0 50.0 . . . .
11:01 60 0 50.0 . . . .
23:01 59 1 50.8 0 0.000 1.000 1.000
24:02 60 0 50.0 . . . .
24:03 60 0 50.0 . . . .
25:01 60 0 50.0 . . . .
26:01 60 0 50.0 . . . .
29:02 60 0 50.0 . . . .
31:01 60 0 50.0 . . . .
32:01 60 0 50.0 . . . .
68:01 60 0 50.0 . . . .
genotype model:
[-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042*
-----
01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000
02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271
02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000
11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000
23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000
24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353
26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612
31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000
32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112
68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
dominant model:
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p
01:01 36 24 -0.14684 -0.117427 0.909
02:01 25 35 -0.32331 -0.000618 0.190
02:06 59 1 -0.14024 0.170057 .
03:01 51 9 -0.05600 -0.583178 0.147
11:01 55 5 -0.19188 0.489815 0.287
23:01 58 2 -0.15400 0.413687 0.281
24:02 49 11 -0.10486 -0.269664 0.537
24:03 59 1 -0.11409 -1.373118 .
25:01 55 5 -0.12237 -0.274749 0.742
26:01 57 3 -0.12473 -0.331558 0.690
29:02 56 4 -0.13044 -0.199941 0.789
31:01 57 3 -0.10097 -0.783003 0.607
32:01 56 4 -0.07702 -0.947791 0.092
68:01 57 3 -0.16915 0.512457 0.196
genotype model:
[-/-] [-/h] [h/h] avg.[-/-] avg.[-/h] avg.[h/h] anova.p
01:01 36 23 1 -0.14684 -0.08833 -0.78655 0.784
02:01 25 27 8 -0.32331 -0.02341 0.07631 0.446
02:06 59 1 0 -0.14024 0.17006 . 0.756
03:01 51 9 0 -0.05600 -0.58318 . 0.138
11:01 55 5 0 -0.19188 0.48981 . 0.137
23:01 58 1 1 -0.15400 0.10762 0.71975 0.663
24:02 49 11 0 -0.10486 -0.26966 . 0.618
24:03 59 1 0 -0.11409 -1.37312 . 0.205
25:01 55 5 0 -0.12237 -0.27475 . 0.742
26:01 57 3 0 -0.12473 -0.33156 . 0.725
29:02 56 4 0 -0.13044 -0.19994 . 0.892
31:01 57 3 0 -0.10097 -0.78300 . 0.243
32:01 56 4 0 -0.07702 -0.94779 . 0.086
68:01 57 3 0 -0.16915 0.51246 . 0.243
Logistic regression (dominant model) with 60 individuals:
glm(case ˜ h, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.792e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -8.777e-16
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.372e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.624e-01
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.418e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 2.874e-15
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.495e+00
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.170e+00
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.282e-01
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.771e+01
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
h.2.5% h.97.5% h.pval
24:02 0.1585 3.4251 0.032*
-----
01:01 -1.0330 1.0330 1.000
02:01 -1.1643 0.8899 0.793
02:06 -2868.1268 2836.9268 0.991
03:01 -1.1624 1.6872 0.718
11:01 -2.3074 1.4237 0.643
23:01 -2.8192 2.8192 1.000
24:03 -2868.1268 2836.9268 0.991
25:01 -3.7498 0.7588 0.194
26:01 -2731.9621 2698.6192 0.990
29:02 -3.4931 1.1530 0.324
31:01 -1.7277 3.1842 0.561
32:01 -3859.2763 3894.6947 0.993
68:01 -2731.9621 2698.6192 0.990
Logistic regression (dominant model) with 60 individuals:
glm(case ˜ h + pc1, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.793e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -2.268e-04
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.370e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.562e+01
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.686e-01
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.451e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 -3.062e-03
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.501e+00
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.189e+00
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.289e-01
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.781e+01
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.673e+01
h.2.5% h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02 0.1587 3.4264 0.032* 0.011111 -0.5249 0.5471 0.968
-----
01:01 -1.0334 1.0330 1.000 -0.005807 -0.5126 0.5010 0.982
02:01 -1.1652 0.8913 0.794 -0.002618 -0.5102 0.5049 0.992
02:06 -2868.1460 2836.9076 0.991 -0.028534 -0.5374 0.4803 0.912
03:01 -1.1813 1.7185 0.717 0.011958 -0.5044 0.5283 0.964
11:01 -2.3225 1.4322 0.642 0.008025 -0.5026 0.5186 0.975
23:01 -2.8348 2.8287 0.998 -0.005857 -0.5148 0.5031 0.982
24:03 -2868.1286 2836.9250 0.991 -0.011249 -0.5182 0.4957 0.965
25:01 -3.7579 0.7568 0.193 -0.025685 -0.5490 0.4976 0.923
26:01 -2731.8901 2698.5450 0.990 -0.014069 -0.5297 0.5015 0.957
29:02 -3.5309 1.1526 0.320 0.033234 -0.4796 0.5461 0.899
31:01 -1.7274 3.1851 0.561 -0.008320 -0.5153 0.4987 0.974
32:01 -3845.6317 3881.2510 0.993 -0.125426 -0.6671 0.4162 0.650
68:01 -2721.2124 2687.7497 0.990 -0.086589 -0.6512 0.4781 0.764
Logistic regression (dominant model) with 60 individuals:
glm(case ˜ h + pc1, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est_OR
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 6.005e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 9.998e-01
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 8.720e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 1.647e-07
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 1.308e+00
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 6.407e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 9.969e-01
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 1.676e-07
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 2.230e-01
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.744e-08
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 3.045e-01
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 2.073e+00
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 5.428e+07
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.416e-08
h.2.5%_OR h.97.5%_OR h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02 1.17200 30.766 0.032* 0.011111 -0.5249 0.5471 0.968
-----
01:01 0.35579 2.809 1.000 -0.005807 -0.5126 0.5010 0.982
02:01 0.31185 2.438 0.794 -0.002618 -0.5102 0.5049 0.992
02:06 0.00000 Inf 0.991 -0.028534 -0.5374 0.4803 0.912
03:01 0.30687 5.576 0.717 0.011958 -0.5044 0.5283 0.964
11:01 0.09803 4.188 0.642 0.008025 -0.5026 0.5186 0.975
23:01 0.05873 16.923 0.998 -0.005857 -0.5148 0.5031 0.982
24:03 0.00000 Inf 0.991 -0.011249 -0.5182 0.4957 0.965
25:01 0.02333 2.131 0.193 -0.025685 -0.5490 0.4976 0.923
26:01 0.00000 Inf 0.990 -0.014069 -0.5297 0.5015 0.957
29:02 0.02928 3.167 0.320 0.033234 -0.4796 0.5461 0.899
31:01 0.17774 24.171 0.561 -0.008320 -0.5153 0.4987 0.974
32:01 0.00000 Inf 0.993 -0.125426 -0.6671 0.4162 0.650
68:01 0.00000 Inf 0.990 -0.086589 -0.6512 0.4781 0.764
Linear regression (dominant model) with 60 individuals:
glm(y ˜ h, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5% h.97.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.02941 -0.4805 0.5393
02:01 25 35 -0.32331 -0.000618 0.190 0.32269 -0.1772 0.8226
02:06 59 1 -0.14024 0.170057 . 0.31030 -1.6397 2.2603
03:01 51 9 -0.05600 -0.583178 0.147 -0.52718 -1.2136 0.1592
11:01 55 5 -0.19188 0.489815 0.287 0.68170 -0.2051 1.5685
23:01 58 2 -0.15400 0.413687 0.281 0.56768 -0.8165 1.9518
24:02 49 11 -0.10486 -0.269664 0.537 -0.16481 -0.8091 0.4795
24:03 59 1 -0.11409 -1.373118 . -1.25903 -3.1835 0.6655
25:01 55 5 -0.12237 -0.274749 0.742 -0.15237 -1.0555 0.7507
26:01 57 3 -0.12473 -0.331558 0.690 -0.20683 -1.3519 0.9383
29:02 56 4 -0.13044 -0.199941 0.789 -0.06950 -1.0709 0.9319
31:01 57 3 -0.10097 -0.783003 0.607 -0.68203 -1.8149 0.4508
32:01 56 4 -0.07702 -0.947791 0.092 -0.87077 -1.8470 0.1054
68:01 57 3 -0.16915 0.512457 0.196 0.68161 -0.4512 1.8145
h.pval
01:01 0.910
02:01 0.211
02:06 0.756
03:01 0.138
11:01 0.137
23:01 0.425
24:02 0.618
24:03 0.205
25:01 0.742
26:01 0.725
29:02 0.892
31:01 0.243
32:01 0.086
68:01 0.243
Linear regression (dominant model) with 60 individuals:
glm(y ˜ h + pc1, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773
02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891
02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722
03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884
11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520
23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598
24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034
24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708
25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388
26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460
29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496
31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254
32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862
68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749
h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386
02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414
02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371
03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539
11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474
23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343
24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393
24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395
25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396
26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390
29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372
31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371
32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210
68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294
Linear regression (dominant model) with 60 individuals:
glm(y ˜ h + pc1, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773
02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891
02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722
03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884
11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520
23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598
24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034
24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708
25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388
26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460
29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496
31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254
32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862
68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749
h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386
02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414
02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371
03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539
11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474
23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343
24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393
24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395
25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396
26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390
29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372
31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371
32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210
68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294
Logistic regression (additive model) with 60 individuals:
glm(case ˜ h, family = binomial, data = data)
[-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p h.est h.2.5%
24:02 109 11 46.8 81.8 3.6030 0.058 0.053 1.7918 0.1585
-----
01:01 95 25 50.5 48.0 0.0000 1.000 1.000 -0.1207 -1.0843
02:01 77 43 48.1 53.5 0.1450 0.703 0.704 0.2137 -0.5289
02:06 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268
03:01 111 9 49.5 55.6 0.0000 1.000 1.000 0.2624 -1.1624
11:01 115 5 50.4 40.0 0.0000 1.000 1.000 -0.4418 -2.3074
23:01 117 3 50.4 33.3 0.0000 1.000 1.000 -0.4323 -2.3435
24:03 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268
25:01 115 5 51.3 20.0 0.8348 0.361 0.364 -1.4955 -3.7498
26:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621
29:02 116 4 50.9 25.0 0.2586 0.611 0.619 -1.1701 -3.4931
31:01 117 3 49.6 66.7 0.0000 1.000 1.000 0.7282 -1.7277
32:01 116 4 48.3 100.0 2.3276 0.127 0.119 17.7092 -3859.2763
68:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621
h.97.5% h.pval
24:02 3.4251 0.032*
-----
01:01 0.8430 0.806
02:01 0.9563 0.573
02:06 2836.9268 0.991
03:01 1.6872 0.718
11:01 1.4237 0.643
23:01 1.4789 0.658
24:03 2836.9268 0.991
25:01 0.7588 0.194
26:01 2698.6192 0.990
29:02 1.1530 0.324
31:01 3.1842 0.561
32:01 3894.6947 0.993
68:01 2698.6192 0.990
Logistic regression (recessive model) with 60 individuals:
glm(case ˜ h, family = binomial, data = data)
[-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p h.est
01:01 59 1 50.8 0 0.000 1.000 1.000 -15.600
02:01 52 8 46.2 75 1.298 0.255 0.254 1.253
02:06 60 0 50.0 . . . . .
03:01 60 0 50.0 . . . . .
11:01 60 0 50.0 . . . . .
23:01 59 1 50.8 0 0.000 1.000 1.000 -15.600
24:02 60 0 50.0 . . . . .
24:03 60 0 50.0 . . . . .
25:01 60 0 50.0 . . . . .
26:01 60 0 50.0 . . . . .
29:02 60 0 50.0 . . . . .
31:01 60 0 50.0 . . . . .
32:01 60 0 50.0 . . . . .
68:01 60 0 50.0 . . . . .
h.2.5% h.97.5% h.pval
01:01 -2868.1268 2836.927 0.991
02:01 -0.4379 2.943 0.146
02:06 . . .
03:01 . . .
11:01 . . .
23:01 -2868.1268 2836.927 0.991
24:02 . . .
24:03 . . .
25:01 . . .
26:01 . . .
29:02 . . .
31:01 . . .
32:01 . . .
68:01 . . .
Logistic regression (genotype model) with 60 individuals:
glm(case ˜ h, family = binomial, data = data)
[-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042*
-----
01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000
02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271
02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000
11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000
23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000
24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353
26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612
31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000
32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112
68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
h1.est h1.2.5% h1.97.5% h1.pval h2.est h2.2.5% h2.97.5%
24:02 1.79176 0.1585 3.4251 0.032* . . .
-----
01:01 0.08701 -0.9600 1.1340 0.871 -15.566 -2868.0929 2836.961
02:01 -0.45474 -1.5524 0.6430 0.417 1.019 -0.7637 2.801
02:06 -15.59997 -2868.1268 2836.9268 0.991 . . .
03:01 0.26236 -1.1624 1.6872 0.718 . . .
11:01 -0.44183 -2.3074 1.4237 0.643 . . .
23:01 16.56607 -4686.4552 4719.5873 0.994 -16.566 -4719.5873 4686.455
24:03 -15.59997 -2868.1268 2836.9268 0.991 . . .
25:01 -1.49549 -3.7498 0.7588 0.194 . . .
26:01 -16.67143 -2731.9621 2698.6192 0.990 . . .
29:02 -1.17007 -3.4931 1.1530 0.324 . . .
31:01 0.72824 -1.7277 3.1842 0.561 . . .
32:01 17.70917 -3859.2763 3894.6947 0.993 . . .
68:01 -16.67143 -2731.9621 2698.6192 0.990 . . .
h2.pval
24:02 .
-----
01:01 0.991
02:01 0.263
02:06 .
03:01 .
11:01 .
23:01 0.994
24:03 .
25:01 .
26:01 .
29:02 .
31:01 .
32:01 .
68:01 .
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 24
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:58
1, SNP: 155, Loss: 186.198, OOB Acc: 68.18%, # of Haplo: 13
2, SNP: 111, Loss: 138.902, OOB Acc: 72.73%, # of Haplo: 13
3, SNP: 159, Loss: 134.624, OOB Acc: 81.82%, # of Haplo: 15
4, SNP: 75, Loss: 120.53, OOB Acc: 86.36%, # of Haplo: 16
5, SNP: 129, Loss: 79.3436, OOB Acc: 90.91%, # of Haplo: 20
6, SNP: 135, Loss: 56.5856, OOB Acc: 90.91%, # of Haplo: 20
7, SNP: 162, Loss: 40.1347, OOB Acc: 90.91%, # of Haplo: 20
8, SNP: 195, Loss: 29.7565, OOB Acc: 90.91%, # of Haplo: 24
9, SNP: 182, Loss: 23.1586, OOB Acc: 90.91%, # of Haplo: 24
10, SNP: 24, Loss: 16.7007, OOB Acc: 90.91%, # of Haplo: 27
11, SNP: 154, Loss: 15.6368, OOB Acc: 90.91%, # of Haplo: 28
12, SNP: 17, Loss: 15.4059, OOB Acc: 90.91%, # of Haplo: 39
13, SNP: 199, Loss: 14.4894, OOB Acc: 90.91%, # of Haplo: 40
14, SNP: 73, Loss: 8.03154, OOB Acc: 90.91%, # of Haplo: 42
15, SNP: 151, Loss: 5.75619, OOB Acc: 90.91%, # of Haplo: 42
[1] 2019-04-13 03:03:58, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 42
1, SNP: 87, Loss: 163.788, OOB Acc: 50.00%, # of Haplo: 15
2, SNP: 179, Loss: 159.036, OOB Acc: 57.69%, # of Haplo: 16
3, SNP: 162, Loss: 127.744, OOB Acc: 61.54%, # of Haplo: 17
4, SNP: 113, Loss: 120.348, OOB Acc: 76.92%, # of Haplo: 17
5, SNP: 149, Loss: 99.7027, OOB Acc: 80.77%, # of Haplo: 19
6, SNP: 183, Loss: 63.9201, OOB Acc: 80.77%, # of Haplo: 19
7, SNP: 82, Loss: 46.9454, OOB Acc: 80.77%, # of Haplo: 19
8, SNP: 73, Loss: 36.2707, OOB Acc: 84.62%, # of Haplo: 21
9, SNP: 47, Loss: 34.9876, OOB Acc: 84.62%, # of Haplo: 22
10, SNP: 140, Loss: 27.9494, OOB Acc: 84.62%, # of Haplo: 22
11, SNP: 189, Loss: 23.9141, OOB Acc: 84.62%, # of Haplo: 23
12, SNP: 249, Loss: 18.884, OOB Acc: 84.62%, # of Haplo: 27
13, SNP: 151, Loss: 15.8511, OOB Acc: 84.62%, # of Haplo: 27
14, SNP: 21, Loss: 11.1982, OOB Acc: 88.46%, # of Haplo: 30
15, SNP: 215, Loss: 10.7762, OOB Acc: 88.46%, # of Haplo: 32
16, SNP: 136, Loss: 10.5472, OOB Acc: 88.46%, # of Haplo: 38
17, SNP: 75, Loss: 10.4276, OOB Acc: 88.46%, # of Haplo: 49
18, SNP: 28, Loss: 10.3043, OOB Acc: 88.46%, # of Haplo: 55
19, SNP: 266, Loss: 10.3043, OOB Acc: 92.31%, # of Haplo: 48
20, SNP: 199, Loss: 9.5327, OOB Acc: 92.31%, # of Haplo: 48
[2] 2019-04-13 03:03:58, OOB Acc: 92.31%, # of SNPs: 20, # of Haplo: 48
1, SNP: 167, Loss: 182.217, OOB Acc: 55.88%, # of Haplo: 14
2, SNP: 126, Loss: 120.313, OOB Acc: 64.71%, # of Haplo: 16
3, SNP: 135, Loss: 94.3559, OOB Acc: 73.53%, # of Haplo: 19
4, SNP: 173, Loss: 73.5379, OOB Acc: 79.41%, # of Haplo: 19
5, SNP: 196, Loss: 47.4931, OOB Acc: 85.29%, # of Haplo: 20
6, SNP: 124, Loss: 28.5275, OOB Acc: 88.24%, # of Haplo: 20
7, SNP: 112, Loss: 27.3051, OOB Acc: 88.24%, # of Haplo: 22
8, SNP: 150, Loss: 26.8966, OOB Acc: 88.24%, # of Haplo: 22
9, SNP: 106, Loss: 20.7953, OOB Acc: 88.24%, # of Haplo: 21
10, SNP: 116, Loss: 20.4753, OOB Acc: 88.24%, # of Haplo: 21
11, SNP: 16, Loss: 20.2453, OOB Acc: 88.24%, # of Haplo: 24
12, SNP: 6, Loss: 20.1953, OOB Acc: 88.24%, # of Haplo: 28
13, SNP: 181, Loss: 19.9842, OOB Acc: 88.24%, # of Haplo: 28
14, SNP: 73, Loss: 11.7972, OOB Acc: 91.18%, # of Haplo: 33
15, SNP: 96, Loss: 11.7743, OOB Acc: 91.18%, # of Haplo: 37
16, SNP: 177, Loss: 11.7597, OOB Acc: 91.18%, # of Haplo: 45
17, SNP: 151, Loss: 11.3816, OOB Acc: 91.18%, # of Haplo: 45
18, SNP: 199, Loss: 10.1586, OOB Acc: 91.18%, # of Haplo: 45
[3] 2019-04-13 03:03:58, OOB Acc: 91.18%, # of SNPs: 18, # of Haplo: 45
1, SNP: 191, Loss: 189.049, OOB Acc: 63.64%, # of Haplo: 15
2, SNP: 100, Loss: 160.313, OOB Acc: 72.73%, # of Haplo: 18
3, SNP: 158, Loss: 135.403, OOB Acc: 77.27%, # of Haplo: 19
4, SNP: 86, Loss: 112.795, OOB Acc: 86.36%, # of Haplo: 22
5, SNP: 134, Loss: 75.8819, OOB Acc: 86.36%, # of Haplo: 24
6, SNP: 85, Loss: 54.3124, OOB Acc: 90.91%, # of Haplo: 29
7, SNP: 128, Loss: 36.9547, OOB Acc: 90.91%, # of Haplo: 29
8, SNP: 77, Loss: 30.0981, OOB Acc: 90.91%, # of Haplo: 32
9, SNP: 54, Loss: 24.7805, OOB Acc: 90.91%, # of Haplo: 32
10, SNP: 21, Loss: 20.945, OOB Acc: 90.91%, # of Haplo: 36
11, SNP: 199, Loss: 20.2197, OOB Acc: 90.91%, # of Haplo: 36
12, SNP: 266, Loss: 19.48, OOB Acc: 90.91%, # of Haplo: 38
13, SNP: 52, Loss: 19.1914, OOB Acc: 90.91%, # of Haplo: 38
14, SNP: 215, Loss: 18.9375, OOB Acc: 90.91%, # of Haplo: 38
15, SNP: 73, Loss: 14.4966, OOB Acc: 90.91%, # of Haplo: 38
16, SNP: 151, Loss: 13.3459, OOB Acc: 90.91%, # of Haplo: 39
[4] 2019-04-13 03:03:58, OOB Acc: 90.91%, # of SNPs: 16, # of Haplo: 39
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001216782 0.0001372891 0.0002777870 0.0023012412 0.0072873069 0.0234246874
Max. Mean SD
0.2886695022 0.0296925310 0.0666389121
Accuracy with training data: 98.5%
Out-of-bag accuracy: 91.3%
Gene: A
Training dataset: 34 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 54
avg. # of SNPs in an individual classifier: 17.25
(sd: 2.22, min: 15, max: 20, median: 17.00)
avg. # of haplotypes in an individual classifier: 43.50
(sd: 3.87, min: 39, max: 48, median: 43.50)
avg. out-of-bag accuracy: 91.33%
(sd: 0.67%, min: 90.91%, max: 92.31%, median: 91.04%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001216782 0.0001372891 0.0002777870 0.0023012412 0.0072873069 0.0234246874
Max. Mean SD
0.2886695022 0.0296925310 0.0666389121
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:58) 0%
Predicting (2019-04-13 03:03:58) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 2 (7.7%) 3 (11.5%) 20 (76.9%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0007878 0.0047578 0.0193435 0.0128352 0.2886695
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:58) 0%
Predicting (2019-04-13 03:03:58) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 2 (7.7%) 3 (11.5%) 20 (76.9%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0007878 0.0047578 0.0193435 0.0128352 0.2886695
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
SNP genotypes:
90 samples X 3932 SNPs
SNPs range from 28694391bp to 33426848bp on hg19
Missing rate per SNP:
min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489
Missing rate per sample:
min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554
Minor allele frequency:
min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144
Allelic information:
A/G C/T G/T A/C C/G A/T
1567 1510 348 332 111 64
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 5316 SNPs from chromosome 6.
SNP genotypes:
90 samples X 5316 SNPs
SNPs range from 25651262bp to 33426848bp on hg19
Missing rate per SNP:
min: 0, max: 0.1, mean: 0.0882054, median: 0.1, sd: 0.030674
Missing rate per sample:
min: 0, max: 0.863619, mean: 0.0882054, median: 0.00131678, sd: 0.259735
Minor allele frequency:
min: 0, max: 0.5, mean: 0.201867, median: 0.179012, sd: 0.155475
Allelic information:
A/G C/T G/T A/C C/G A/T
2102 2046 480 471 134 83
Exclude 1 monomorphic SNP
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 9
# of SNPs: 77, # of samples: 60
# of unique HLA alleles: 12
[-] 2019-04-13 03:03:59
[1] 2019-04-13 03:03:59, OOB Acc: 98.00%, # of SNPs: 13, # of Haplo: 20
[2] 2019-04-13 03:03:59, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 21
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02
Max. Mean SD
4.728168e-01 4.409445e-02 1.068815e-01
Accuracy with training data: 95.0%
Out-of-bag accuracy: 94.5%
Gene: DQB1
Training dataset: 60 samples X 77 SNPs
# of HLA alleles: 12
# of individual classifiers: 2
total # of SNPs used: 20
avg. # of SNPs in an individual classifier: 14.00
(sd: 1.41, min: 13, max: 15, median: 14.00)
avg. # of haplotypes in an individual classifier: 20.50
(sd: 0.71, min: 20, max: 21, median: 20.50)
avg. out-of-bag accuracy: 94.45%
(sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02
Max. Mean SD
4.728168e-01 4.409445e-02 1.068815e-01
Genome assembly: hg19
The HIBAG model:
There are 77 SNP predictors in total.
There are 2 individual classifiers.
Summarize the missing fractions of SNP predictors per classifier:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 60
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 100
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 100
# of unique HLA alleles: 0
# of unique HLA genotypes: 0
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 200
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Exclude 9 monomorphic SNPs
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:59
[1] 2019-04-13 03:03:59, OOB Acc: 86.96%, # of SNPs: 12, # of Haplo: 32
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
4.166789e-14 4.261245e-14 5.111347e-14 2.589270e-03 1.608934e-02 5.868848e-02
Max. Mean SD
6.267394e-01 6.664806e-02 1.405453e-01
Accuracy with training data: 94.2%
Out-of-bag accuracy: 87.0%
Exclude 9 monomorphic SNPs
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:59
[1] 2019-04-13 03:03:59, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 40
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.894066e-24 9.219565e-20 9.218854e-19 2.189685e-03 7.704546e-03 2.406258e-02
Max. Mean SD
2.755151e-01 2.949891e-02 6.162169e-02
Accuracy with training data: 95.0%
Out-of-bag accuracy: 87.5%
Gene: A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 24
avg. # of SNPs in an individual classifier: 13.50
(sd: 2.12, min: 12, max: 15, median: 13.50)
avg. # of haplotypes in an individual classifier: 36.00
(sd: 5.66, min: 32, max: 40, median: 36.00)
avg. out-of-bag accuracy: 87.23%
(sd: 0.38%, min: 86.96%, max: 87.50%, median: 87.23%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
9.233104e-13 5.204084e-10 5.195775e-09 2.309655e-03 1.448839e-02 3.746431e-02
Max. Mean SD
4.511273e-01 4.807348e-02 1.006148e-01
Genome assembly: hg19
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 24
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:59
1, SNP: 155, Loss: 186.198, OOB Acc: 68.18%, # of Haplo: 13
2, SNP: 111, Loss: 138.902, OOB Acc: 72.73%, # of Haplo: 13
3, SNP: 159, Loss: 134.624, OOB Acc: 81.82%, # of Haplo: 15
4, SNP: 75, Loss: 120.53, OOB Acc: 86.36%, # of Haplo: 16
5, SNP: 129, Loss: 79.3436, OOB Acc: 90.91%, # of Haplo: 20
6, SNP: 135, Loss: 56.5856, OOB Acc: 90.91%, # of Haplo: 20
7, SNP: 162, Loss: 40.1347, OOB Acc: 90.91%, # of Haplo: 20
8, SNP: 195, Loss: 29.7565, OOB Acc: 90.91%, # of Haplo: 24
9, SNP: 182, Loss: 23.1586, OOB Acc: 90.91%, # of Haplo: 24
10, SNP: 24, Loss: 16.7007, OOB Acc: 90.91%, # of Haplo: 27
11, SNP: 154, Loss: 15.6368, OOB Acc: 90.91%, # of Haplo: 28
12, SNP: 17, Loss: 15.4059, OOB Acc: 90.91%, # of Haplo: 39
13, SNP: 199, Loss: 14.4894, OOB Acc: 90.91%, # of Haplo: 40
14, SNP: 73, Loss: 8.03154, OOB Acc: 90.91%, # of Haplo: 42
15, SNP: 151, Loss: 5.75619, OOB Acc: 90.91%, # of Haplo: 42
[1] 2019-04-13 03:03:59, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 42
1, SNP: 87, Loss: 163.788, OOB Acc: 50.00%, # of Haplo: 15
2, SNP: 179, Loss: 159.036, OOB Acc: 57.69%, # of Haplo: 16
3, SNP: 162, Loss: 127.744, OOB Acc: 61.54%, # of Haplo: 17
4, SNP: 113, Loss: 120.348, OOB Acc: 76.92%, # of Haplo: 17
5, SNP: 149, Loss: 99.7027, OOB Acc: 80.77%, # of Haplo: 19
6, SNP: 183, Loss: 63.9201, OOB Acc: 80.77%, # of Haplo: 19
7, SNP: 82, Loss: 46.9454, OOB Acc: 80.77%, # of Haplo: 19
8, SNP: 73, Loss: 36.2707, OOB Acc: 84.62%, # of Haplo: 21
9, SNP: 47, Loss: 34.9876, OOB Acc: 84.62%, # of Haplo: 22
10, SNP: 140, Loss: 27.9494, OOB Acc: 84.62%, # of Haplo: 22
11, SNP: 189, Loss: 23.9141, OOB Acc: 84.62%, # of Haplo: 23
12, SNP: 249, Loss: 18.884, OOB Acc: 84.62%, # of Haplo: 27
13, SNP: 151, Loss: 15.8511, OOB Acc: 84.62%, # of Haplo: 27
14, SNP: 21, Loss: 11.1982, OOB Acc: 88.46%, # of Haplo: 30
15, SNP: 215, Loss: 10.7762, OOB Acc: 88.46%, # of Haplo: 32
16, SNP: 136, Loss: 10.5472, OOB Acc: 88.46%, # of Haplo: 38
17, SNP: 75, Loss: 10.4276, OOB Acc: 88.46%, # of Haplo: 49
18, SNP: 28, Loss: 10.3043, OOB Acc: 88.46%, # of Haplo: 55
19, SNP: 266, Loss: 10.3043, OOB Acc: 92.31%, # of Haplo: 48
20, SNP: 199, Loss: 9.5327, OOB Acc: 92.31%, # of Haplo: 48
[2] 2019-04-13 03:03:59, OOB Acc: 92.31%, # of SNPs: 20, # of Haplo: 48
1, SNP: 167, Loss: 182.217, OOB Acc: 55.88%, # of Haplo: 14
2, SNP: 126, Loss: 120.313, OOB Acc: 64.71%, # of Haplo: 16
3, SNP: 135, Loss: 94.3559, OOB Acc: 73.53%, # of Haplo: 19
4, SNP: 173, Loss: 73.5379, OOB Acc: 79.41%, # of Haplo: 19
5, SNP: 196, Loss: 47.4931, OOB Acc: 85.29%, # of Haplo: 20
6, SNP: 124, Loss: 28.5275, OOB Acc: 88.24%, # of Haplo: 20
7, SNP: 112, Loss: 27.3051, OOB Acc: 88.24%, # of Haplo: 22
8, SNP: 150, Loss: 26.8966, OOB Acc: 88.24%, # of Haplo: 22
9, SNP: 106, Loss: 20.7953, OOB Acc: 88.24%, # of Haplo: 21
10, SNP: 116, Loss: 20.4753, OOB Acc: 88.24%, # of Haplo: 21
11, SNP: 16, Loss: 20.2453, OOB Acc: 88.24%, # of Haplo: 24
12, SNP: 6, Loss: 20.1953, OOB Acc: 88.24%, # of Haplo: 28
13, SNP: 181, Loss: 19.9842, OOB Acc: 88.24%, # of Haplo: 28
14, SNP: 73, Loss: 11.7972, OOB Acc: 91.18%, # of Haplo: 33
15, SNP: 96, Loss: 11.7743, OOB Acc: 91.18%, # of Haplo: 37
16, SNP: 177, Loss: 11.7597, OOB Acc: 91.18%, # of Haplo: 45
17, SNP: 151, Loss: 11.3816, OOB Acc: 91.18%, # of Haplo: 45
18, SNP: 199, Loss: 10.1586, OOB Acc: 91.18%, # of Haplo: 45
[3] 2019-04-13 03:03:59, OOB Acc: 91.18%, # of SNPs: 18, # of Haplo: 45
1, SNP: 191, Loss: 189.049, OOB Acc: 63.64%, # of Haplo: 15
2, SNP: 100, Loss: 160.313, OOB Acc: 72.73%, # of Haplo: 18
3, SNP: 158, Loss: 135.403, OOB Acc: 77.27%, # of Haplo: 19
4, SNP: 86, Loss: 112.795, OOB Acc: 86.36%, # of Haplo: 22
5, SNP: 134, Loss: 75.8819, OOB Acc: 86.36%, # of Haplo: 24
6, SNP: 85, Loss: 54.3124, OOB Acc: 90.91%, # of Haplo: 29
7, SNP: 128, Loss: 36.9547, OOB Acc: 90.91%, # of Haplo: 29
8, SNP: 77, Loss: 30.0981, OOB Acc: 90.91%, # of Haplo: 32
9, SNP: 54, Loss: 24.7805, OOB Acc: 90.91%, # of Haplo: 32
10, SNP: 21, Loss: 20.945, OOB Acc: 90.91%, # of Haplo: 36
11, SNP: 199, Loss: 20.2197, OOB Acc: 90.91%, # of Haplo: 36
12, SNP: 266, Loss: 19.48, OOB Acc: 90.91%, # of Haplo: 38
13, SNP: 52, Loss: 19.1914, OOB Acc: 90.91%, # of Haplo: 38
14, SNP: 215, Loss: 18.9375, OOB Acc: 90.91%, # of Haplo: 38
15, SNP: 73, Loss: 14.4966, OOB Acc: 90.91%, # of Haplo: 38
16, SNP: 151, Loss: 13.3459, OOB Acc: 90.91%, # of Haplo: 39
[4] 2019-04-13 03:03:59, OOB Acc: 90.91%, # of SNPs: 16, # of Haplo: 39
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001216782 0.0001372891 0.0002777870 0.0023012412 0.0072873069 0.0234246874
Max. Mean SD
0.2886695022 0.0296925310 0.0666389121
Accuracy with training data: 98.5%
Out-of-bag accuracy: 91.3%
Gene: A
Training dataset: 34 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 54
avg. # of SNPs in an individual classifier: 17.25
(sd: 2.22, min: 15, max: 20, median: 17.00)
avg. # of haplotypes in an individual classifier: 43.50
(sd: 3.87, min: 39, max: 48, median: 43.50)
avg. out-of-bag accuracy: 91.33%
(sd: 0.67%, min: 90.91%, max: 92.31%, median: 91.04%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001216782 0.0001372891 0.0002777870 0.0023012412 0.0072873069 0.0234246874
Max. Mean SD
0.2886695022 0.0296925310 0.0666389121
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:59) 0%
Predicting (2019-04-13 03:03:59) 100%
Allelic ambiguity: 01:01, 02:02
Allelic ambiguity: 01:01, 02:02
Allelic ambiguity: 09:01
Allelic ambiguity: 09:01
Allelic ambiguity: 05:01, 06:01
Allelic ambiguity: 05:01, 06:01
Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01
Pos Num * - A D E F G H I K L M N Q R S T V W Y
1 120 120 . . . . . . . . . . . . . . . . . . .
9 120 . 81 . . . . . . . . . . . . . 15 7 . . 17
44 120 . 25 . . . . . . . . . . . . 95 . . . . .
56 120 . 117 . . . . . . . . . . . . 3 . . . . .
62 120 . 46 . . 15 . 44 . . . 4 . . . 11 . . . . .
63 120 . 105 . . . . . . . . . . 11 4 . . . . . .
65 120 . 105 . . . . 15 . . . . . . . . . . . . .
66 120 . 61 . . . . . . . 59 . . . . . . . . . .
67 120 . 25 . . . . . . . . . . . . . . . 95 . .
70 120 . 99 . . . . . . . . . . . 21 . . . . . .
73 120 . 117 . . . . . . 3 . . . . . . . . . . .
74 120 . 76 . . . . . 44 . . . . . . . . . . . .
76 120 . 32 . . 24 . . . . . . . . . . . . 64 . .
77 120 . 47 . 64 . . . . . . . . . . . 9 . . . .
79 120 . 96 . . . . . . . . . . . . 24 . . . . .
80 120 . 96 . . . . . . 24 . . . . . . . . . . .
81 120 . 96 24 . . . . . . . . . . . . . . . . .
82 120 . 96 . . . . . . . . 24 . . . . . . . . .
83 120 . 96 . . . . . . . . . . . . 24 . . . . .
90 120 . 38 82 . . . . . . . . . . . . . . . . .
95 120 . 61 . . . . . . . . 15 . . . . . . 44 . .
97 120 . 39 . . . . . . . . . 29 . . 52 . . . . .
99 120 . 105 . . . 15 . . . . . . . . . . . . . .
105 120 . 42 . . . . . . . . . . . . . 78 . . . .
107 120 . 76 . . . . . . . . . . . . . . . . 44 .
109 120 . 116 . . . . . . . . 4 . . . . . . . . .
114 120 . 46 . . . . . 59 . . . . . 15 . . . . . .
116 120 . 61 . . . . . . . . . . . . . . . . . 59
127 120 . 58 . . . . . . . 62 . . . . . . . . . .
142 120 . 73 . . . . . . . . . . . . . . 47 . . .
144 120 . 98 . . . . . . . . . . . 22 . . . . . .
145 120 . 73 . . . . . 47 . . . . . . . . . . . .
149 120 . 112 . . . . . . . . . . . . . . 8 . . .
150 120 . 25 95 . . . . . . . . . . . . . . . . .
151 120 . 106 . . . . . . . . . . . . 14 . . . . .
152 120 . 30 . . 17 . . . . . . . . . . . . 73 . .
156 120 . 25 . . . . . . . . 67 . . 17 . . . . 11 .
158 120 . 25 95 . . . . . . . . . . . . . . . . .
161 120 . 111 . 9 . . . . . . . . . . . . . . . .
163 120 . 38 . . . . . . . . . . . . . . 82 . . .
166 120 . 39 . . 81 . . . . . . . . . . . . . . .
167 120 . 39 . . . . . . . . . . . . . . . . 81 .
183 120 120 . . . . . . . . . . . . . . . . . . .
Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01
Pos Num * - A D E F G H I K L M N Q R S T V W Y
-23 120 120 . . . . . . . . . . . . . . . . . . .
-22 120 120 . . . . . . . . . . . . . . . . . . .
-21 120 120 . . . . . . . . . . . . . . . . . . .
-20 120 120 . . . . . . . . . . . . . . . . . . .
-19 120 120 . . . . . . . . . . . . . . . . . . .
-18 120 120 . . . . . . . . . . . . . . . . . . .
-17 120 120 . . . . . . . . . . . . . . . . . . .
-16 120 120 . . . . . . . . . . . . . . . . . . .
-15 120 120 . . . . . . . . . . . . . . . . . . .
-14 120 120 . . . . . . . . . . . . . . . . . . .
-13 120 120 . . . . . . . . . . . . . . . . . . .
-12 120 120 . . . . . . . . . . . . . . . . . . .
-11 120 120 . . . . . . . . . . . . . . . . . . .
-10 120 120 . . . . . . . . . . . . . . . . . . .
-9 120 120 . . . . . . . . . . . . . . . . . . .
-8 120 120 . . . . . . . . . . . . . . . . . . .
-7 120 120 . . . . . . . . . . . . . . . . . . .
-6 120 120 . . . . . . . . . . . . . . . . . . .
-5 120 120 . . . . . . . . . . . . . . . . . . .
-4 120 120 . . . . . . . . . . . . . . . . . . .
-3 120 120 . . . . . . . . . . . . . . . . . . .
-2 120 120 . . . . . . . . . . . . . . . . . . .
-1 120 120 . . . . . . . . . . . . . . . . . . .
. 120 120 . . . . . . . . . . . . . . . . . . .
1 120 120 . . . . . . . . . . . . . . . . . . .
9 120 . 81 . . . . . . . . . . . . . 15 7 . . 17
44 120 . 25 . . . . . . . . . . . . 95 . . . . .
56 120 . 117 . . . . . . . . . . . . 3 . . . . .
62 120 . 46 . . 15 . 44 . . . 4 . . . 11 . . . . .
63 120 . 105 . . . . . . . . . . 11 4 . . . . . .
65 120 . 105 . . . . 15 . . . . . . . . . . . . .
66 120 . 61 . . . . . . . 59 . . . . . . . . . .
67 120 . 25 . . . . . . . . . . . . . . . 95 . .
70 120 . 99 . . . . . . . . . . . 21 . . . . . .
73 120 . 117 . . . . . . 3 . . . . . . . . . . .
74 120 . 76 . . . . . 44 . . . . . . . . . . . .
76 120 . 32 . . 24 . . . . . . . . . . . . 64 . .
77 120 . 47 . 64 . . . . . . . . . . . 9 . . . .
79 120 . 96 . . . . . . . . . . . . 24 . . . . .
80 120 . 96 . . . . . . 24 . . . . . . . . . . .
81 120 . 96 24 . . . . . . . . . . . . . . . . .
82 120 . 96 . . . . . . . . 24 . . . . . . . . .
83 120 . 96 . . . . . . . . . . . . 24 . . . . .
90 120 . 38 82 . . . . . . . . . . . . . . . . .
95 120 . 61 . . . . . . . . 15 . . . . . . 44 . .
97 120 . 39 . . . . . . . . . 29 . . 52 . . . . .
99 120 . 105 . . . 15 . . . . . . . . . . . . . .
105 120 . 42 . . . . . . . . . . . . . 78 . . . .
107 120 . 76 . . . . . . . . . . . . . . . . 44 .
109 120 . 116 . . . . . . . . 4 . . . . . . . . .
114 120 . 46 . . . . . 59 . . . . . 15 . . . . . .
116 120 . 61 . . . . . . . . . . . . . . . . . 59
127 120 . 58 . . . . . . . 62 . . . . . . . . . .
142 120 . 73 . . . . . . . . . . . . . . 47 . . .
144 120 . 98 . . . . . . . . . . . 22 . . . . . .
145 120 . 73 . . . . . 47 . . . . . . . . . . . .
149 120 . 112 . . . . . . . . . . . . . . 8 . . .
150 120 . 25 95 . . . . . . . . . . . . . . . . .
151 120 . 106 . . . . . . . . . . . . 14 . . . . .
152 120 . 30 . . 17 . . . . . . . . . . . . 73 . .
156 120 . 25 . . . . . . . . 67 . . 17 . . . . 11 .
158 120 . 25 95 . . . . . . . . . . . . . . . . .
161 120 . 111 . 9 . . . . . . . . . . . . . . . .
163 120 . 38 . . . . . . . . . . . . . . 82 . . .
166 120 . 39 . . 81 . . . . . . . . . . . . . . .
167 120 . 39 . . . . . . . . . . . . . . . . 81 .
183 120 120 . . . . . . . . . . . . . . . . . . .
184 120 120 . . . . . . . . . . . . . . . . . . .
185 120 120 . . . . . . . . . . . . . . . . . . .
186 120 120 . . . . . . . . . . . . . . . . . . .
187 120 120 . . . . . . . . . . . . . . . . . . .
188 120 120 . . . . . . . . . . . . . . . . . . .
189 120 120 . . . . . . . . . . . . . . . . . . .
190 120 120 . . . . . . . . . . . . . . . . . . .
191 120 120 . . . . . . . . . . . . . . . . . . .
192 120 120 . . . . . . . . . . . . . . . . . . .
193 120 120 . . . . . . . . . . . . . . . . . . .
194 120 120 . . . . . . . . . . . . . . . . . . .
195 120 120 . . . . . . . . . . . . . . . . . . .
196 120 120 . . . . . . . . . . . . . . . . . . .
197 120 120 . . . . . . . . . . . . . . . . . . .
198 120 120 . . . . . . . . . . . . . . . . . . .
199 120 120 . . . . . . . . . . . . . . . . . . .
200 120 120 . . . . . . . . . . . . . . . . . . .
201 120 120 . . . . . . . . . . . . . . . . . . .
202 120 120 . . . . . . . . . . . . . . . . . . .
203 120 120 . . . . . . . . . . . . . . . . . . .
204 120 120 . . . . . . . . . . . . . . . . . . .
205 120 120 . . . . . . . . . . . . . . . . . . .
206 120 120 . . . . . . . . . . . . . . . . . . .
207 120 120 . . . . . . . . . . . . . . . . . . .
208 120 120 . . . . . . . . . . . . . . . . . . .
209 120 120 . . . . . . . . . . . . . . . . . . .
210 120 120 . . . . . . . . . . . . . . . . . . .
211 120 120 . . . . . . . . . . . . . . . . . . .
212 120 120 . . . . . . . . . . . . . . . . . . .
213 120 120 . . . . . . . . . . . . . . . . . . .
214 120 120 . . . . . . . . . . . . . . . . . . .
215 120 120 . . . . . . . . . . . . . . . . . . .
216 120 120 . . . . . . . . . . . . . . . . . . .
217 120 120 . . . . . . . . . . . . . . . . . . .
218 120 120 . . . . . . . . . . . . . . . . . . .
219 120 120 . . . . . . . . . . . . . . . . . . .
220 120 120 . . . . . . . . . . . . . . . . . . .
221 120 120 . . . . . . . . . . . . . . . . . . .
222 120 120 . . . . . . . . . . . . . . . . . . .
223 120 120 . . . . . . . . . . . . . . . . . . .
224 120 120 . . . . . . . . . . . . . . . . . . .
225 120 120 . . . . . . . . . . . . . . . . . . .
226 120 120 . . . . . . . . . . . . . . . . . . .
227 120 120 . . . . . . . . . . . . . . . . . . .
228 120 120 . . . . . . . . . . . . . . . . . . .
229 120 120 . . . . . . . . . . . . . . . . . . .
230 120 120 . . . . . . . . . . . . . . . . . . .
231 120 120 . . . . . . . . . . . . . . . . . . .
232 120 120 . . . . . . . . . . . . . . . . . . .
233 120 120 . . . . . . . . . . . . . . . . . . .
234 120 120 . . . . . . . . . . . . . . . . . . .
235 120 120 . . . . . . . . . . . . . . . . . . .
236 120 120 . . . . . . . . . . . . . . . . . . .
237 120 120 . . . . . . . . . . . . . . . . . . .
238 120 120 . . . . . . . . . . . . . . . . . . .
239 120 120 . . . . . . . . . . . . . . . . . . .
240 120 120 . . . . . . . . . . . . . . . . . . .
241 120 120 . . . . . . . . . . . . . . . . . . .
242 120 120 . . . . . . . . . . . . . . . . . . .
243 120 120 . . . . . . . . . . . . . . . . . . .
244 120 120 . . . . . . . . . . . . . . . . . . .
245 120 120 . . . . . . . . . . . . . . . . . . .
246 120 120 . . . . . . . . . . . . . . . . . . .
247 120 120 . . . . . . . . . . . . . . . . . . .
248 120 120 . . . . . . . . . . . . . . . . . . .
249 120 120 . . . . . . . . . . . . . . . . . . .
250 120 120 . . . . . . . . . . . . . . . . . . .
251 120 120 . . . . . . . . . . . . . . . . . . .
252 120 120 . . . . . . . . . . . . . . . . . . .
253 120 120 . . . . . . . . . . . . . . . . . . .
254 120 120 . . . . . . . . . . . . . . . . . . .
255 120 120 . . . . . . . . . . . . . . . . . . .
256 120 120 . . . . . . . . . . . . . . . . . . .
257 120 120 . . . . . . . . . . . . . . . . . . .
258 120 120 . . . . . . . . . . . . . . . . . . .
259 120 120 . . . . . . . . . . . . . . . . . . .
260 120 120 . . . . . . . . . . . . . . . . . . .
261 120 120 . . . . . . . . . . . . . . . . . . .
262 120 120 . . . . . . . . . . . . . . . . . . .
263 120 120 . . . . . . . . . . . . . . . . . . .
264 120 120 . . . . . . . . . . . . . . . . . . .
265 120 120 . . . . . . . . . . . . . . . . . . .
266 120 120 . . . . . . . . . . . . . . . . . . .
267 120 120 . . . . . . . . . . . . . . . . . . .
268 120 120 . . . . . . . . . . . . . . . . . . .
269 120 120 . . . . . . . . . . . . . . . . . . .
270 120 120 . . . . . . . . . . . . . . . . . . .
271 120 120 . . . . . . . . . . . . . . . . . . .
272 120 120 . . . . . . . . . . . . . . . . . . .
273 120 120 . . . . . . . . . . . . . . . . . . .
274 120 120 . . . . . . . . . . . . . . . . . . .
275 120 120 . . . . . . . . . . . . . . . . . . .
276 120 120 . . . . . . . . . . . . . . . . . . .
277 120 120 . . . . . . . . . . . . . . . . . . .
278 120 120 . . . . . . . . . . . . . . . . . . .
279 120 120 . . . . . . . . . . . . . . . . . . .
280 120 120 . . . . . . . . . . . . . . . . . . .
281 120 120 . . . . . . . . . . . . . . . . . . .
282 120 120 . . . . . . . . . . . . . . . . . . .
283 120 120 . . . . . . . . . . . . . . . . . . .
284 120 120 . . . . . . . . . . . . . . . . . . .
285 120 120 . . . . . . . . . . . . . . . . . . .
286 120 120 . . . . . . . . . . . . . . . . . . .
287 120 120 . . . . . . . . . . . . . . . . . . .
288 120 120 . . . . . . . . . . . . . . . . . . .
289 120 120 . . . . . . . . . . . . . . . . . . .
290 120 120 . . . . . . . . . . . . . . . . . . .
291 120 120 . . . . . . . . . . . . . . . . . . .
292 120 120 . . . . . . . . . . . . . . . . . . .
293 120 120 . . . . . . . . . . . . . . . . . . .
294 120 120 . . . . . . . . . . . . . . . . . . .
295 120 120 . . . . . . . . . . . . . . . . . . .
296 120 120 . . . . . . . . . . . . . . . . . . .
297 120 120 . . . . . . . . . . . . . . . . . . .
298 120 120 . . . . . . . . . . . . . . . . . . .
299 120 120 . . . . . . . . . . . . . . . . . . .
300 120 120 . . . . . . . . . . . . . . . . . . .
301 120 120 . . . . . . . . . . . . . . . . . . .
302 120 120 . . . . . . . . . . . . . . . . . . .
303 120 120 . . . . . . . . . . . . . . . . . . .
304 120 120 . . . . . . . . . . . . . . . . . . .
305 120 120 . . . . . . . . . . . . . . . . . . .
306 120 120 . . . . . . . . . . . . . . . . . . .
307 120 120 . . . . . . . . . . . . . . . . . . .
308 120 120 . . . . . . . . . . . . . . . . . . .
309 120 120 . . . . . . . . . . . . . . . . . . .
310 120 120 . . . . . . . . . . . . . . . . . . .
311 120 120 . . . . . . . . . . . . . . . . . . .
312 120 120 . . . . . . . . . . . . . . . . . . .
313 120 120 . . . . . . . . . . . . . . . . . . .
314 120 120 . . . . . . . . . . . . . . . . . . .
315 120 120 . . . . . . . . . . . . . . . . . . .
316 120 120 . . . . . . . . . . . . . . . . . . .
317 120 120 . . . . . . . . . . . . . . . . . . .
318 120 120 . . . . . . . . . . . . . . . . . . .
319 120 120 . . . . . . . . . . . . . . . . . . .
320 120 120 . . . . . . . . . . . . . . . . . . .
321 120 120 . . . . . . . . . . . . . . . . . . .
322 120 120 . . . . . . . . . . . . . . . . . . .
323 120 120 . . . . . . . . . . . . . . . . . . .
324 120 120 . . . . . . . . . . . . . . . . . . .
325 120 120 . . . . . . . . . . . . . . . . . . .
326 120 120 . . . . . . . . . . . . . . . . . . .
327 120 120 . . . . . . . . . . . . . . . . . . .
328 120 120 . . . . . . . . . . . . . . . . . . .
329 120 120 . . . . . . . . . . . . . . . . . . .
330 120 120 . . . . . . . . . . . . . . . . . . .
331 120 120 . . . . . . . . . . . . . . . . . . .
332 120 120 . . . . . . . . . . . . . . . . . . .
333 120 120 . . . . . . . . . . . . . . . . . . .
334 120 120 . . . . . . . . . . . . . . . . . . .
335 120 120 . . . . . . . . . . . . . . . . . . .
336 120 120 . . . . . . . . . . . . . . . . . . .
337 120 120 . . . . . . . . . . . . . . . . . . .
338 120 120 . . . . . . . . . . . . . . . . . . .
339 120 120 . . . . . . . . . . . . . . . . . . .
340 120 120 . . . . . . . . . . . . . . . . . . .
341 120 120 . . . . . . . . . . . . . . . . . . .
Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02
Pos Num * - A D E F G I K L M N P Q R S T Y
5 120 112 . . . . . . . . . . 8 . . . . . .
6 120 20 92 8 . . . . . . . . . . . . . . .
7 112 20 92 . . . . . . . . . . . . . . . .
8 112 20 92 . . . . . . . . . . . . . . . .
9 112 3 76 . . . 33 . . . . . . . . . . . .
10 112 3 109 . . . . . . . . . . . . . . . .
11 112 3 109 . . . . . . . . . . . . . . . .
12 112 3 109 . . . . . . . . . . . . . . . .
13 112 3 93 16 . . . . . . . . . . . . . . .
14 112 3 14 . . . . . . . . 95 . . . . . . .
15 112 3 109 . . . . . . . . . . . . . . . .
16 112 3 109 . . . . . . . . . . . . . . . .
17 112 3 109 . . . . . . . . . . . . . . . .
18 112 3 109 . . . . . . . . . . . . . . . .
19 112 3 109 . . . . . . . . . . . . . . . .
20 112 3 109 . . . . . . . . . . . . . . . .
26 112 . 20 . . . . . . . 76 . . . . . . . 16
28 112 . 100 . . . . . . . . . . . . . 12 . .
30 112 . 24 . . . . . . . . . . . . . 12 . 76
37 112 . 100 . . . . . 12 . . . . . . . . . .
38 112 . 29 83 . . . . . . . . . . . . . . .
45 112 . 96 . . 16 . . . . . . . . . . . . .
46 112 . 100 . . 12 . . . . . . . . . . . . .
47 112 . 100 . . . 12 . . . . . . . . . . . .
52 112 . 100 . . . . . . . 12 . . . . . . . .
53 112 . 54 . . . . . . . 58 . . . . . . . .
55 112 . 57 . . . . . . . 12 . . 43 . . . . .
56 112 . 109 . . . . . . . 3 . . . . . . . .
57 112 . 14 33 64 . . . . . . . . . . . 1 . .
66 112 . 97 . 15 . . . . . . . . . . . . . .
67 112 . 97 . . . . . 15 . . . . . . . . . .
70 112 3 50 . . 3 . . . . . . . . . 56 . . .
71 112 3 14 . 3 . . . . 12 . . . . . . . 80 .
72 112 3 109 . . . . . . . . . . . . . . . .
73 112 3 109 . . . . . . . . . . . . . . . .
74 112 3 17 12 . 80 . . . . . . . . . . . . .
75 112 3 29 . . . . . . . 80 . . . . . . . .
76 112 3 109 . . . . . . . . . . . . . . . .
77 112 3 26 . . . . . . . . . . . . . . 83 .
78 112 3 109 . . . . . . . . . . . . . . . .
79 112 3 109 . . . . . . . . . . . . . . . .
80 112 3 109 . . . . . . . . . . . . . . . .
81 112 3 109 . . . . . . . . . . . . . . . .
82 112 3 109 . . . . . . . . . . . . . . . .
83 112 3 109 . . . . . . . . . . . . . . . .
84 112 3 51 . . . . . . . . . . . 58 . . . .
85 112 3 51 . . . . . . . 58 . . . . . . . .
86 112 3 50 . . 58 . 1 . . . . . . . . . . .
87 112 3 15 . . . 36 . . . 58 . . . . . . . .
88 112 3 109 . . . . . . . . . . . . . . . .
89 112 3 51 . . . . . . . . . . . . . . 58 .
90 112 3 51 . . . . . . . . . . . . . . 58 .
91 112 3 109 . . . . . . . . . . . . . . . .
92 112 3 109 . . . . . . . . . . . . . . . .
93 112 3 109 . . . . . . . . . . . . . . . .
94 112 17 95 . . . . . . . . . . . . . . . .
95 112 112 . . . . . . . . . . . . . . . . .
Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02
Pos Num * - A D E F G I K L M N P Q R S T Y
-31 120 112 . . . . . . . . . . 8 . . . . . .
-30 120 112 . 8 . . . . . . . . . . . . . . .
-29 112 112 . . . . . . . . . . . . . . . . .
-28 112 112 . . . . . . . . . . . . . . . . .
-27 112 112 . . . . . . . . . . . . . . . . .
-26 112 112 . . . . . . . . . . . . . . . . .
-25 112 112 . . . . . . . . . . . . . . . . .
-24 112 112 . . . . . . . . . . . . . . . . .
-23 112 112 . . . . . . . . . . . . . . . . .
-22 112 112 . . . . . . . . . . . . . . . . .
-21 112 112 . . . . . . . . . . . . . . . . .
-20 112 112 . . . . . . . . . . . . . . . . .
-19 112 112 . . . . . . . . . . . . . . . . .
-18 112 112 . . . . . . . . . . . . . . . . .
-17 112 112 . . . . . . . . . . . . . . . . .
-16 112 112 . . . . . . . . . . . . . . . . .
-15 112 112 . . . . . . . . . . . . . . . . .
-14 112 112 . . . . . . . . . . . . . . . . .
-13 112 112 . . . . . . . . . . . . . . . . .
-12 112 112 . . . . . . . . . . . . . . . . .
-11 112 112 . . . . . . . . . . . . . . . . .
-10 112 112 . . . . . . . . . . . . . . . . .
-9 112 112 . . . . . . . . . . . . . . . . .
-8 112 112 . . . . . . . . . . . . . . . . .
-7 112 112 . . . . . . . . . . . . . . . . .
-6 112 112 . . . . . . . . . . . . . . . . .
-5 112 112 . . . . . . . . . . . . . . . . .
-4 112 112 . . . . . . . . . . . . . . . . .
-3 112 112 . . . . . . . . . . . . . . . . .
-2 112 112 . . . . . . . . . . . . . . . . .
-1 112 112 . . . . . . . . . . . . . . . . .
. 112 112 . . . . . . . . . . . . . . . . .
1 112 112 . . . . . . . . . . . . . . . . .
2 112 112 . . . . . . . . . . . . . . . . .
3 112 112 . . . . . . . . . . . . . . . . .
4 112 112 . . . . . . . . . . . . . . . . .
5 112 112 . . . . . . . . . . . . . . . . .
6 112 20 92 . . . . . . . . . . . . . . . .
7 112 20 92 . . . . . . . . . . . . . . . .
8 112 20 92 . . . . . . . . . . . . . . . .
9 112 3 76 . . . 33 . . . . . . . . . . . .
10 112 3 109 . . . . . . . . . . . . . . . .
11 112 3 109 . . . . . . . . . . . . . . . .
12 112 3 109 . . . . . . . . . . . . . . . .
13 112 3 93 16 . . . . . . . . . . . . . . .
14 112 3 14 . . . . . . . . 95 . . . . . . .
15 112 3 109 . . . . . . . . . . . . . . . .
16 112 3 109 . . . . . . . . . . . . . . . .
17 112 3 109 . . . . . . . . . . . . . . . .
18 112 3 109 . . . . . . . . . . . . . . . .
19 112 3 109 . . . . . . . . . . . . . . . .
20 112 3 109 . . . . . . . . . . . . . . . .
26 112 . 20 . . . . . . . 76 . . . . . . . 16
28 112 . 100 . . . . . . . . . . . . . 12 . .
30 112 . 24 . . . . . . . . . . . . . 12 . 76
37 112 . 100 . . . . . 12 . . . . . . . . . .
38 112 . 29 83 . . . . . . . . . . . . . . .
45 112 . 96 . . 16 . . . . . . . . . . . . .
46 112 . 100 . . 12 . . . . . . . . . . . . .
47 112 . 100 . . . 12 . . . . . . . . . . . .
52 112 . 100 . . . . . . . 12 . . . . . . . .
53 112 . 54 . . . . . . . 58 . . . . . . . .
55 112 . 57 . . . . . . . 12 . . 43 . . . . .
56 112 . 109 . . . . . . . 3 . . . . . . . .
57 112 . 14 33 64 . . . . . . . . . . . 1 . .
66 112 . 97 . 15 . . . . . . . . . . . . . .
67 112 . 97 . . . . . 15 . . . . . . . . . .
70 112 3 50 . . 3 . . . . . . . . . 56 . . .
71 112 3 14 . 3 . . . . 12 . . . . . . . 80 .
72 112 3 109 . . . . . . . . . . . . . . . .
73 112 3 109 . . . . . . . . . . . . . . . .
74 112 3 17 12 . 80 . . . . . . . . . . . . .
75 112 3 29 . . . . . . . 80 . . . . . . . .
76 112 3 109 . . . . . . . . . . . . . . . .
77 112 3 26 . . . . . . . . . . . . . . 83 .
78 112 3 109 . . . . . . . . . . . . . . . .
79 112 3 109 . . . . . . . . . . . . . . . .
80 112 3 109 . . . . . . . . . . . . . . . .
81 112 3 109 . . . . . . . . . . . . . . . .
82 112 3 109 . . . . . . . . . . . . . . . .
83 112 3 109 . . . . . . . . . . . . . . . .
84 112 3 51 . . . . . . . . . . . 58 . . . .
85 112 3 51 . . . . . . . 58 . . . . . . . .
86 112 3 50 . . 58 . 1 . . . . . . . . . . .
87 112 3 15 . . . 36 . . . 58 . . . . . . . .
88 112 3 109 . . . . . . . . . . . . . . . .
89 112 3 51 . . . . . . . . . . . . . . 58 .
90 112 3 51 . . . . . . . . . . . . . . 58 .
91 112 3 109 . . . . . . . . . . . . . . . .
92 112 3 109 . . . . . . . . . . . . . . . .
93 112 3 109 . . . . . . . . . . . . . . . .
94 112 17 95 . . . . . . . . . . . . . . . .
95 112 112 . . . . . . . . . . . . . . . . .
96 112 112 . . . . . . . . . . . . . . . . .
97 112 112 . . . . . . . . . . . . . . . . .
98 112 112 . . . . . . . . . . . . . . . . .
99 112 112 . . . . . . . . . . . . . . . . .
100 112 112 . . . . . . . . . . . . . . . . .
101 112 112 . . . . . . . . . . . . . . . . .
102 112 112 . . . . . . . . . . . . . . . . .
103 112 112 . . . . . . . . . . . . . . . . .
104 112 112 . . . . . . . . . . . . . . . . .
105 112 112 . . . . . . . . . . . . . . . . .
106 112 112 . . . . . . . . . . . . . . . . .
107 112 112 . . . . . . . . . . . . . . . . .
108 112 112 . . . . . . . . . . . . . . . . .
109 112 112 . . . . . . . . . . . . . . . . .
110 112 112 . . . . . . . . . . . . . . . . .
111 112 112 . . . . . . . . . . . . . . . . .
112 112 112 . . . . . . . . . . . . . . . . .
113 112 112 . . . . . . . . . . . . . . . . .
114 112 112 . . . . . . . . . . . . . . . . .
115 112 112 . . . . . . . . . . . . . . . . .
116 112 112 . . . . . . . . . . . . . . . . .
117 112 112 . . . . . . . . . . . . . . . . .
118 112 112 . . . . . . . . . . . . . . . . .
119 112 112 . . . . . . . . . . . . . . . . .
120 112 112 . . . . . . . . . . . . . . . . .
121 112 112 . . . . . . . . . . . . . . . . .
122 112 112 . . . . . . . . . . . . . . . . .
123 112 112 . . . . . . . . . . . . . . . . .
124 112 112 . . . . . . . . . . . . . . . . .
125 112 112 . . . . . . . . . . . . . . . . .
126 112 112 . . . . . . . . . . . . . . . . .
127 112 112 . . . . . . . . . . . . . . . . .
128 112 112 . . . . . . . . . . . . . . . . .
129 112 112 . . . . . . . . . . . . . . . . .
130 112 112 . . . . . . . . . . . . . . . . .
131 112 112 . . . . . . . . . . . . . . . . .
132 112 112 . . . . . . . . . . . . . . . . .
133 112 112 . . . . . . . . . . . . . . . . .
134 112 112 . . . . . . . . . . . . . . . . .
135 112 112 . . . . . . . . . . . . . . . . .
136 112 112 . . . . . . . . . . . . . . . . .
137 112 112 . . . . . . . . . . . . . . . . .
138 112 112 . . . . . . . . . . . . . . . . .
139 112 112 . . . . . . . . . . . . . . . . .
140 112 112 . . . . . . . . . . . . . . . . .
141 112 112 . . . . . . . . . . . . . . . . .
142 112 112 . . . . . . . . . . . . . . . . .
143 112 112 . . . . . . . . . . . . . . . . .
144 112 112 . . . . . . . . . . . . . . . . .
145 112 112 . . . . . . . . . . . . . . . . .
146 112 112 . . . . . . . . . . . . . . . . .
147 112 112 . . . . . . . . . . . . . . . . .
148 112 112 . . . . . . . . . . . . . . . . .
149 112 112 . . . . . . . . . . . . . . . . .
150 112 112 . . . . . . . . . . . . . . . . .
151 112 112 . . . . . . . . . . . . . . . . .
152 112 112 . . . . . . . . . . . . . . . . .
153 112 112 . . . . . . . . . . . . . . . . .
154 112 112 . . . . . . . . . . . . . . . . .
155 112 112 . . . . . . . . . . . . . . . . .
156 112 112 . . . . . . . . . . . . . . . . .
157 112 112 . . . . . . . . . . . . . . . . .
158 112 112 . . . . . . . . . . . . . . . . .
159 112 112 . . . . . . . . . . . . . . . . .
160 112 112 . . . . . . . . . . . . . . . . .
161 112 112 . . . . . . . . . . . . . . . . .
162 112 112 . . . . . . . . . . . . . . . . .
163 112 112 . . . . . . . . . . . . . . . . .
164 112 112 . . . . . . . . . . . . . . . . .
165 112 112 . . . . . . . . . . . . . . . . .
166 112 112 . . . . . . . . . . . . . . . . .
167 112 112 . . . . . . . . . . . . . . . . .
168 112 112 . . . . . . . . . . . . . . . . .
169 112 112 . . . . . . . . . . . . . . . . .
170 112 112 . . . . . . . . . . . . . . . . .
171 112 112 . . . . . . . . . . . . . . . . .
172 112 112 . . . . . . . . . . . . . . . . .
173 112 112 . . . . . . . . . . . . . . . . .
174 112 112 . . . . . . . . . . . . . . . . .
175 112 112 . . . . . . . . . . . . . . . . .
176 112 112 . . . . . . . . . . . . . . . . .
177 112 112 . . . . . . . . . . . . . . . . .
178 112 112 . . . . . . . . . . . . . . . . .
179 112 112 . . . . . . . . . . . . . . . . .
180 112 112 . . . . . . . . . . . . . . . . .
181 112 112 . . . . . . . . . . . . . . . . .
182 112 112 . . . . . . . . . . . . . . . . .
183 112 112 . . . . . . . . . . . . . . . . .
184 112 112 . . . . . . . . . . . . . . . . .
185 112 112 . . . . . . . . . . . . . . . . .
186 112 112 . . . . . . . . . . . . . . . . .
187 112 112 . . . . . . . . . . . . . . . . .
188 112 112 . . . . . . . . . . . . . . . . .
189 112 112 . . . . . . . . . . . . . . . . .
190 112 112 . . . . . . . . . . . . . . . . .
191 112 112 . . . . . . . . . . . . . . . . .
192 112 112 . . . . . . . . . . . . . . . . .
193 112 112 . . . . . . . . . . . . . . . . .
194 112 112 . . . . . . . . . . . . . . . . .
195 112 112 . . . . . . . . . . . . . . . . .
196 112 112 . . . . . . . . . . . . . . . . .
197 112 112 . . . . . . . . . . . . . . . . .
198 112 112 . . . . . . . . . . . . . . . . .
199 112 112 . . . . . . . . . . . . . . . . .
200 112 112 . . . . . . . . . . . . . . . . .
201 112 112 . . . . . . . . . . . . . . . . .
202 112 112 . . . . . . . . . . . . . . . . .
203 112 112 . . . . . . . . . . . . . . . . .
204 112 112 . . . . . . . . . . . . . . . . .
205 112 112 . . . . . . . . . . . . . . . . .
206 112 112 . . . . . . . . . . . . . . . . .
207 112 112 . . . . . . . . . . . . . . . . .
208 112 112 . . . . . . . . . . . . . . . . .
209 112 112 . . . . . . . . . . . . . . . . .
210 112 112 . . . . . . . . . . . . . . . . .
211 112 112 . . . . . . . . . . . . . . . . .
212 112 112 . . . . . . . . . . . . . . . . .
213 112 112 . . . . . . . . . . . . . . . . .
214 112 112 . . . . . . . . . . . . . . . . .
215 112 112 . . . . . . . . . . . . . . . . .
216 112 112 . . . . . . . . . . . . . . . . .
217 112 112 . . . . . . . . . . . . . . . . .
218 112 112 . . . . . . . . . . . . . . . . .
219 112 112 . . . . . . . . . . . . . . . . .
220 112 112 . . . . . . . . . . . . . . . . .
221 112 112 . . . . . . . . . . . . . . . . .
222 112 112 . . . . . . . . . . . . . . . . .
223 112 112 . . . . . . . . . . . . . . . . .
224 112 112 . . . . . . . . . . . . . . . . .
225 112 112 . . . . . . . . . . . . . . . . .
226 112 112 . . . . . . . . . . . . . . . . .
227 112 112 . . . . . . . . . . . . . . . . .
228 112 112 . . . . . . . . . . . . . . . . .
229 112 112 . . . . . . . . . . . . . . . . .
230 112 112 . . . . . . . . . . . . . . . . .
231 112 112 . . . . . . . . . . . . . . . . .
232 112 112 . . . . . . . . . . . . . . . . .
233 112 112 . . . . . . . . . . . . . . . . .
234 112 112 . . . . . . . . . . . . . . . . .
235 112 112 . . . . . . . . . . . . . . . . .
236 112 112 . . . . . . . . . . . . . . . . .
237 112 112 . . . . . . . . . . . . . . . . .
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
Exclude 9 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:04:02
[1] 2019-04-13 03:04:02, OOB Acc: 86.96%, # of SNPs: 12, # of Haplo: 32
[2] 2019-04-13 03:04:02, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 40
[3] 2019-04-13 03:04:02, OOB Acc: 97.92%, # of SNPs: 14, # of Haplo: 21
[4] 2019-04-13 03:04:02, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 25
[5] 2019-04-13 03:04:02, OOB Acc: 78.95%, # of SNPs: 14, # of Haplo: 21
[6] 2019-04-13 03:04:02, OOB Acc: 93.75%, # of SNPs: 16, # of Haplo: 22
[7] 2019-04-13 03:04:03, OOB Acc: 93.75%, # of SNPs: 24, # of Haplo: 81
[8] 2019-04-13 03:04:03, OOB Acc: 92.86%, # of SNPs: 20, # of Haplo: 45
[9] 2019-04-13 03:04:03, OOB Acc: 94.74%, # of SNPs: 16, # of Haplo: 45
[10] 2019-04-13 03:04:03, OOB Acc: 97.37%, # of SNPs: 15, # of Haplo: 40
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001493079 0.0001617044 0.0002732730 0.0039571951 0.0150902995 0.0320853535
Max. Mean SD
0.3770420992 0.0416903408 0.0825371577
Accuracy with training data: 98.3%
Out-of-bag accuracy: 91.9%
Gene: A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 10
total # of SNPs used: 95
avg. # of SNPs in an individual classifier: 16.00
(sd: 3.50, min: 12, max: 24, median: 15.00)
avg. # of haplotypes in an individual classifier: 37.20
(sd: 18.22, min: 21, max: 81, median: 36.00)
avg. out-of-bag accuracy: 91.92%
(sd: 5.83%, min: 78.95%, max: 97.92%, median: 93.75%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001493079 0.0001617044 0.0002732730 0.0039571951 0.0150902995 0.0320853535
Max. Mean SD
0.3770420992 0.0416903408 0.0825371577
Genome assembly: hg19
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
Loading required namespace: gdsfmt
Loading required namespace: SNPRelate
Import 2348 SNPs from chromosome 6.
3 SNPs with invalid alleles have been removed.
SNP genotypes:
165 samples X 2345 SNPs
SNPs range from 24652946bp to 33524089bp on hg18
Missing rate per SNP:
min: 0, max: 0.0484848, mean: 0.001698, median: 0, sd: 0.00504919
Missing rate per sample:
min: 0, max: 0.0110874, mean: 0.001698, median: 0.000852878, sd: 0.00195105
Minor allele frequency:
min: 0, max: 0.5, mean: 0.194167, median: 0.172727, sd: 0.149319
Allelic information:
A/G T/C G/A C/T T/G A/C C/A G/T C/G G/C A/T T/A
556 467 410 400 116 109 103 81 30 29 28 16
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
No allelic strand orders are switched.
SNP genotypes:
150 samples X 1214 SNPs
SNPs range from 28695148bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0866667, mean: 0.0844646, median: 0.0866667, sd: 0.0128841
Missing rate per sample:
min: 0, max: 0.968699, mean: 0.0844646, median: 0.000823723, sd: 0.273119
Minor allele frequency:
min: 0, max: 0.5, mean: 0.234168, median: 0.218978, sd: 0.137855
Allelic information:
A/G C/T G/T A/C
505 496 109 104
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1197 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0657059, median: 0.0666667, sd: 0.00757446
Missing rate per sample:
min: 0, max: 0.978279, mean: 0.0657059, median: 0.000835422, sd: 0.245786
Minor allele frequency:
min: 0.101695, max: 0.5, mean: 0.278734, median: 0.267857, sd: 0.117338
Allelic information:
A/G C/T A/C G/T
511 476 105 105
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
SNP genotypes:
90 samples X 3932 SNPs
SNPs range from 28694391bp to 33426848bp on hg19
Missing rate per SNP:
min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489
Missing rate per sample:
min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554
Minor allele frequency:
min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144
Allelic information:
A/G C/T G/T A/C C/G A/T
1567 1510 348 332 111 64
No allelic strand orders are switched.
SNP genotypes:
60 samples X 1214 SNPs
SNPs range from 28695148bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0650879, median: 0.0666667, sd: 0.0097381
Missing rate per sample:
min: 0, max: 0.968699, mean: 0.0650879, median: 0.000823723, sd: 0.243373
Minor allele frequency:
min: 0, max: 0.5, mean: 0.234476, median: 0.223214, sd: 0.13833
Allelic information:
A/G C/T G/T A/C
505 496 109 104
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
MAF filter (>=0.01), excluding 9 SNP(s)
using the default genome assembly (assembly="hg19")
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:04:05
[1] 2019-04-13 03:04:05, OOB Acc: 92.00%, # of SNPs: 24, # of Haplo: 29
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.222247e-28 1.128571e-24 1.128371e-23 6.944660e-04 8.333349e-03 3.673611e-02
Max. Mean SD
9.105734e-02 2.054649e-02 2.598603e-02
Accuracy with training data: 96.7%
Out-of-bag accuracy: 92.0%
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:04:05
[1] 2019-04-13 03:04:06, OOB Acc: 97.50%, # of SNPs: 18, # of Haplo: 34
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
5.014366e-13 4.671716e-10 4.667203e-09 1.640727e-03 7.504546e-03 2.126745e-02
Max. Mean SD
9.784316e-02 1.490504e-02 1.947399e-02
Accuracy with training data: 97.5%
Out-of-bag accuracy: 97.5%
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:04:06
[1] 2019-04-13 03:04:06, OOB Acc: 88.89%, # of SNPs: 14, # of Haplo: 38
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.222223e-18 6.603163e-16 6.583163e-15 1.944468e-03 1.020834e-02 4.122739e-02
Max. Mean SD
1.808372e-01 2.422083e-02 3.699146e-02
Accuracy with training data: 95.8%
Out-of-bag accuracy: 88.9%
Gene: C
Training dataset: 60 samples X 83 SNPs
# of HLA alleles: 17
# of individual classifiers: 3
total # of SNPs used: 40
avg. # of SNPs in an individual classifier: 18.67
(sd: 5.03, min: 14, max: 24, median: 18.00)
avg. # of haplotypes in an individual classifier: 33.67
(sd: 4.51, min: 29, max: 38, median: 34.00)
avg. out-of-bag accuracy: 92.80%
(sd: 4.36%, min: 88.89%, max: 97.50%, median: 92.00%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.708707e-13 1.229313e-05 1.229313e-04 1.860746e-03 9.050936e-03 3.332722e-02
Max. Mean SD
1.210500e-01 1.989079e-02 2.507466e-02
Genome assembly: hg19
Exclude 1 monomorphic SNP
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 9
# of SNPs: 77, # of samples: 60
# of unique HLA alleles: 12
[-] 2019-04-13 03:04:06
[1] 2019-04-13 03:04:06, OOB Acc: 98.00%, # of SNPs: 13, # of Haplo: 20
[2] 2019-04-13 03:04:06, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 21
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02
Max. Mean SD
4.728168e-01 4.409445e-02 1.068815e-01
Accuracy with training data: 95.0%
Out-of-bag accuracy: 94.5%
Gene: DQB1
Training dataset: 60 samples X 77 SNPs
# of HLA alleles: 12
# of individual classifiers: 2
total # of SNPs used: 20
avg. # of SNPs in an individual classifier: 14.00
(sd: 1.41, min: 13, max: 15, median: 14.00)
avg. # of haplotypes in an individual classifier: 20.50
(sd: 0.71, min: 20, max: 21, median: 20.50)
avg. out-of-bag accuracy: 94.45%
(sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02
Max. Mean SD
4.728168e-01 4.409445e-02 1.068815e-01
Genome assembly: hg19
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:04:06
[1] 2019-04-13 03:04:06, OOB Acc: 86.96%, # of SNPs: 12, # of Haplo: 32
[2] 2019-04-13 03:04:06, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 40
[3] 2019-04-13 03:04:06, OOB Acc: 97.92%, # of SNPs: 14, # of Haplo: 21
[4] 2019-04-13 03:04:06, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 25
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777
Max. Mean SD
0.3714064677 0.0407831671 0.0808783377
Accuracy with training data: 99.2%
Out-of-bag accuracy: 92.0%
Gene: A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 42
avg. # of SNPs in an individual classifier: 13.75
(sd: 1.26, min: 12, max: 15, median: 14.00)
avg. # of haplotypes in an individual classifier: 29.50
(sd: 8.35, min: 21, max: 40, median: 28.50)
avg. out-of-bag accuracy: 91.96%
(sd: 5.56%, min: 86.96%, max: 97.92%, median: 91.48%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777
Max. Mean SD
0.3714064677 0.0407831671 0.0808783377
Genome assembly: hg19
Gene: A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 42
avg. # of SNPs in an individual classifier: 13.75
(sd: 1.26, min: 12, max: 15, median: 14.00)
avg. # of haplotypes in an individual classifier: 29.50
(sd: 8.35, min: 21, max: 40, median: 28.50)
avg. out-of-bag accuracy: 91.96%
(sd: 5.56%, min: 86.96%, max: 97.92%, median: 91.48%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777
Max. Mean SD
0.3714064677 0.0407831671 0.0808783377
Genome assembly: hg19
Sat Apr 13 03:04:06 2019, passing the 1/4 classifiers.
Sat Apr 13 03:04:06 2019, passing the 2/4 classifiers.
Sat Apr 13 03:04:06 2019, passing the 3/4 classifiers.
Sat Apr 13 03:04:06 2019, passing the 4/4 classifiers.
Allele Num. Freq. CR ACC SEN SPE PPV NPV Miscall
Valid. Valid. (%) (%) (%) (%) (%) (%) (%)
----
Overall accuracy: 92.0%, Call rate: 100.0%
01:01 25 0.2083 100.0 100.0 100.0 100.0 100.0 100.0 --
02:01 43 0.3583 100.0 96.7 100.0 95.1 92.5 100.0 --
02:06 1 0.0083 25.0 97.7 0.0 100.0 -- 97.7 02:01 (100)
03:01 9 0.0750 100.0 100.0 100.0 100.0 100.0 100.0 --
11:01 5 0.0417 100.0 100.0 100.0 100.0 100.0 100.0 --
23:01 3 0.0250 100.0 98.4 75.0 100.0 100.0 98.4 24:02 (100)
24:02 11 0.0917 100.0 97.3 100.0 97.1 76.2 100.0 --
24:03 1 0.0083 100.0 97.8 0.0 100.0 -- 97.8 24:02 (75)
25:01 5 0.0417 100.0 98.4 100.0 98.3 84.7 100.0 --
26:01 3 0.0250 100.0 98.4 62.5 100.0 100.0 98.4 25:01 (83)
29:02 4 0.0333 100.0 97.8 75.0 100.0 100.0 97.8 02:01 (75)
31:01 3 0.0250 75.0 100.0 100.0 100.0 100.0 100.0 --
32:01 4 0.0333 100.0 100.0 100.0 100.0 100.0 100.0 --
68:01 3 0.0250 100.0 100.0 100.0 100.0 100.0 100.0 --
\title{Imputation Evaluation}
\documentclass[12pt]{article}
\usepackage{fullpage}
\usepackage{longtable}
\begin{document}
\maketitle
\setlength{\LTcapwidth}{6.5in}
% -------- BEGIN TABLE --------
\begin{longtable}{rrr | rrrrrrl}
\caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).}
\label{tab:accuracy} \\
Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endfirsthead
\multicolumn{10}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\
Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endhead
\hline
\multicolumn{10}{r}{Continued on next page ...} \\
\hline
\endfoot
\hline\hline
\endlastfoot
\multicolumn{10}{l}{\it Overall accuracy: 92.0\%, Call rate: 100.0\%} \\
01:01 & 25 & 0.2083 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
02:01 & 43 & 0.3583 & 100.0 & 96.7 & 100.0 & 95.1 & 92.5 & 100.0 & -- \\
02:06 & 1 & 0.0083 & 25.0 & 97.7 & 0.0 & 100.0 & -- & 97.7 & 02:01 (100) \\
03:01 & 9 & 0.0750 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
11:01 & 5 & 0.0417 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
23:01 & 3 & 0.0250 & 100.0 & 98.4 & 75.0 & 100.0 & 100.0 & 98.4 & 24:02 (100) \\
24:02 & 11 & 0.0917 & 100.0 & 97.3 & 100.0 & 97.1 & 76.2 & 100.0 & -- \\
24:03 & 1 & 0.0083 & 100.0 & 97.8 & 0.0 & 100.0 & -- & 97.8 & 24:02 (75) \\
25:01 & 5 & 0.0417 & 100.0 & 98.4 & 100.0 & 98.3 & 84.7 & 100.0 & -- \\
26:01 & 3 & 0.0250 & 100.0 & 98.4 & 62.5 & 100.0 & 100.0 & 98.4 & 25:01 (83) \\
29:02 & 4 & 0.0333 & 100.0 & 97.8 & 75.0 & 100.0 & 100.0 & 97.8 & 02:01 (75) \\
31:01 & 3 & 0.0250 & 75.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
32:01 & 4 & 0.0333 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
68:01 & 3 & 0.0250 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
\end{longtable}
% -------- END TABLE --------
\end{document}
<!DOCTYPE html>
<html>
<head>
<title>Imputation Evaluation</title>
</head>
<body>
<h1>Imputation Evaluation</h1>
<p></p>
<h3><b>Table 1L:</b> The sensitivity (SEN), specificity (SPE),
positive predictive value (PPV), negative predictive value (NPV)
and call rate (CR).</h3>
<table id="TB-Acc" class="tabular" border="1" CELLSPACING="1">
<tr>
<th>Allele </th> <th>Num. Valid.</th> <th>Freq. Valid.</th> <th>CR (%)</th> <th>ACC (%)</th> <th>SEN (%)</th> <th>SPE (%)</th> <th>PPV (%)</th> <th>NPV (%)</th> <th>Miscall (%)</th>
</tr>
<tr>
<td colspan="10">
<i> Overall accuracy: 92.0%, Call rate: 100.0% </i>
</td>
</tr>
<tr>
<td>01:01</td> <td>25</td> <td>0.2083</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:01</td> <td>43</td> <td>0.3583</td> <td>100.0</td> <td>96.7</td> <td>100.0</td> <td>95.1</td> <td>92.5</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:06</td> <td>1</td> <td>0.0083</td> <td>25.0</td> <td>97.7</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>97.7</td> <td>02:01 (100)</td>
</tr>
<tr>
<td>03:01</td> <td>9</td> <td>0.0750</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>11:01</td> <td>5</td> <td>0.0417</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>23:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>98.4</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>98.4</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>24:02</td> <td>11</td> <td>0.0917</td> <td>100.0</td> <td>97.3</td> <td>100.0</td> <td>97.1</td> <td>76.2</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>24:03</td> <td>1</td> <td>0.0083</td> <td>100.0</td> <td>97.8</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>97.8</td> <td>24:02 (75)</td>
</tr>
<tr>
<td>25:01</td> <td>5</td> <td>0.0417</td> <td>100.0</td> <td>98.4</td> <td>100.0</td> <td>98.3</td> <td>84.7</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>26:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>98.4</td> <td>62.5</td> <td>100.0</td> <td>100.0</td> <td>98.4</td> <td>25:01 (83)</td>
</tr>
<tr>
<td>29:02</td> <td>4</td> <td>0.0333</td> <td>100.0</td> <td>97.8</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>97.8</td> <td>02:01 (75)</td>
</tr>
<tr>
<td>31:01</td> <td>3</td> <td>0.0250</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>32:01</td> <td>4</td> <td>0.0333</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>68:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
</table>
</body>
</html>
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 24
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Build a HIBAG model of 4 individual classifiers in parallel with 2 compute nodes:
2019-04-13 03:04:07, 1, job 2, # of SNPs: 13, # of haplo: 25, acc: 77.3%
-- avg out-of-bag acc: 77.27%, sd: NA%, min: 77.27%, max: 77.27%
2019-04-13 03:04:07, 2, job 1, # of SNPs: 15, # of haplo: 29, acc: 68.2%
-- avg out-of-bag acc: 72.73%, sd: 6.43%, min: 68.18%, max: 77.27%
2019-04-13 03:04:07, 3, job 1, # of SNPs: 11, # of haplo: 20, acc: 75.0%
-- avg out-of-bag acc: 73.48%, sd: 4.73%, min: 68.18%, max: 77.27%
2019-04-13 03:04:07, 4, job 2, # of SNPs: 16, # of haplo: 48, acc: 92.3%
-- avg out-of-bag acc: 78.19%, sd: 10.17%, min: 68.18%, max: 92.31%
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0005947299 0.0006009795 0.0006572256 0.0035994277 0.0077211451 0.0217891437
Max. Mean SD
0.3112910741 0.0363824443 0.0749359074
Accuracy with training data: 97.1%
Out-of-bag accuracy: 78.2%
Build a HIBAG model of 4 individual classifiers in parallel with 2 compute nodes:
The model is autosaved in 'tmp_model.RData'.
2019-04-13 03:04:08, 1, job 2, # of SNPs: 15, # of haplo: 28, acc: 79.2%
-- avg out-of-bag acc: 79.17%, sd: NA%, min: 79.17%, max: 79.17%
2019-04-13 03:04:08, 2, job 1, # of SNPs: 16, # of haplo: 43, acc: 87.5%
-- avg out-of-bag acc: 83.33%, sd: 5.89%, min: 79.17%, max: 87.50%
2019-04-13 03:04:08, 3, job 1, # of SNPs: 14, # of haplo: 31, acc: 87.5%
Stop "job 1".
-- avg out-of-bag acc: 84.72%, sd: 4.81%, min: 79.17%, max: 87.50%
2019-04-13 03:04:08, 4, job 1, # of SNPs: 12, # of haplo: 40, acc: 87.5%
Stop "job 1".
-- avg out-of-bag acc: 85.42%, sd: 4.17%, min: 79.17%, max: 87.50%
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0005406630 0.0005513682 0.0006477149 0.0032481280 0.0069351986 0.0129974119
Max. Mean SD
0.3957005129 0.0344092581 0.0907182263
Accuracy with training data: 97.1%
Out-of-bag accuracy: 85.4%
Gene: A
Training dataset: 34 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 48
avg. # of SNPs in an individual classifier: 14.25
(sd: 1.71, min: 12, max: 16, median: 14.50)
avg. # of haplotypes in an individual classifier: 35.50
(sd: 7.14, min: 28, max: 43, median: 35.50)
avg. out-of-bag accuracy: 85.42%
(sd: 4.17%, min: 79.17%, max: 87.50%, median: 87.50%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0005406630 0.0005513682 0.0006477149 0.0032481280 0.0069351986 0.0129974119
Max. Mean SD
0.3957005129 0.0344092581 0.0907182263
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:04:08) 0%
Predicting (2019-04-13 03:04:08) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
2 (7.7%) 2 (7.7%) 5 (19.2%) 17 (65.4%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0006556 0.0045330 0.0231821 0.0136119 0.3957005
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Run in parallel with 2 compute nodes.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
2 (7.7%) 2 (7.7%) 5 (19.2%) 17 (65.4%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0006556 0.0045330 0.0231821 0.0136119 0.3957005
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:04:09
1, SNP: 123, Loss: 203.122, OOB Acc: 54.55%, # of Haplo: 14
2, SNP: 182, Loss: 155.176, OOB Acc: 63.64%, # of Haplo: 16
3, SNP: 106, Loss: 123.308, OOB Acc: 77.27%, # of Haplo: 16
4, SNP: 87, Loss: 77.8709, OOB Acc: 81.82%, # of Haplo: 20
5, SNP: 114, Loss: 43.8425, OOB Acc: 90.91%, # of Haplo: 20
6, SNP: 7, Loss: 36.5344, OOB Acc: 90.91%, # of Haplo: 23
7, SNP: 199, Loss: 26.705, OOB Acc: 90.91%, # of Haplo: 24
8, SNP: 201, Loss: 16.5421, OOB Acc: 90.91%, # of Haplo: 26
9, SNP: 102, Loss: 15.7295, OOB Acc: 90.91%, # of Haplo: 33
10, SNP: 144, Loss: 15.078, OOB Acc: 90.91%, # of Haplo: 33
11, SNP: 52, Loss: 14.8361, OOB Acc: 90.91%, # of Haplo: 42
12, SNP: 108, Loss: 14.7598, OOB Acc: 90.91%, # of Haplo: 42
13, SNP: 73, Loss: 11.7396, OOB Acc: 90.91%, # of Haplo: 43
14, SNP: 151, Loss: 11.3074, OOB Acc: 90.91%, # of Haplo: 45
[1] 2019-04-13 03:04:09, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 45
1, SNP: 45, Loss: 180.067, OOB Acc: 50.00%, # of Haplo: 15
2, SNP: 75, Loss: 135.995, OOB Acc: 60.71%, # of Haplo: 18
3, SNP: 176, Loss: 100.071, OOB Acc: 64.29%, # of Haplo: 24
4, SNP: 181, Loss: 72.877, OOB Acc: 67.86%, # of Haplo: 34
5, SNP: 154, Loss: 60.9088, OOB Acc: 71.43%, # of Haplo: 35
6, SNP: 180, Loss: 56.0063, OOB Acc: 71.43%, # of Haplo: 39
7, SNP: 164, Loss: 51.6779, OOB Acc: 71.43%, # of Haplo: 48
8, SNP: 227, Loss: 47.3381, OOB Acc: 71.43%, # of Haplo: 50
9, SNP: 197, Loss: 40.1255, OOB Acc: 71.43%, # of Haplo: 50
10, SNP: 165, Loss: 38.8164, OOB Acc: 71.43%, # of Haplo: 49
11, SNP: 151, Loss: 34.5811, OOB Acc: 71.43%, # of Haplo: 49
12, SNP: 222, Loss: 34.3146, OOB Acc: 71.43%, # of Haplo: 53
13, SNP: 111, Loss: 34.3145, OOB Acc: 75.00%, # of Haplo: 61
14, SNP: 73, Loss: 27.4168, OOB Acc: 75.00%, # of Haplo: 61
15, SNP: 132, Loss: 27.3856, OOB Acc: 75.00%, # of Haplo: 61
16, SNP: 199, Loss: 25.7831, OOB Acc: 75.00%, # of Haplo: 61
[2] 2019-04-13 03:04:09, OOB Acc: 75.00%, # of SNPs: 16, # of Haplo: 61
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.632299e-08 2.701820e-06 2.678129e-05 1.297587e-03 1.145581e-02 1.694929e-02
Max. Mean SD
3.194254e-01 2.861672e-02 7.433563e-02
Accuracy with training data: 97.1%
Out-of-bag accuracy: 83.0%
Exclude 9 monomorphic SNPs
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:04:09
1, SNP: 119, Loss: 233.718, OOB Acc: 50.00%, # of Haplo: 13
2, SNP: 232, Loss: 181.18, OOB Acc: 52.78%, # of Haplo: 16
3, SNP: 71, Loss: 139.471, OOB Acc: 58.33%, # of Haplo: 18
4, SNP: 166, Loss: 116.525, OOB Acc: 61.11%, # of Haplo: 20
5, SNP: 116, Loss: 87.4554, OOB Acc: 63.89%, # of Haplo: 24
6, SNP: 126, Loss: 42.9667, OOB Acc: 66.67%, # of Haplo: 30
7, SNP: 33, Loss: 32.6021, OOB Acc: 66.67%, # of Haplo: 41
8, SNP: 102, Loss: 30.9058, OOB Acc: 69.44%, # of Haplo: 41
9, SNP: 169, Loss: 30.4075, OOB Acc: 72.22%, # of Haplo: 41
10, SNP: 148, Loss: 29.993, OOB Acc: 72.22%, # of Haplo: 41
11, SNP: 87, Loss: 29.5166, OOB Acc: 72.22%, # of Haplo: 41
12, SNP: 70, Loss: 24.11, OOB Acc: 72.22%, # of Haplo: 41
13, SNP: 151, Loss: 23.5686, OOB Acc: 72.22%, # of Haplo: 42
[1] 2019-04-13 03:04:09, OOB Acc: 72.22%, # of SNPs: 13, # of Haplo: 42
1, SNP: 102, Loss: 142.289, OOB Acc: 38.46%, # of Haplo: 15
2, SNP: 75, Loss: 110.14, OOB Acc: 46.15%, # of Haplo: 19
3, SNP: 214, Loss: 91.7293, OOB Acc: 53.85%, # of Haplo: 19
4, SNP: 121, Loss: 63.0626, OOB Acc: 57.69%, # of Haplo: 19
5, SNP: 194, Loss: 34.2678, OOB Acc: 57.69%, # of Haplo: 23
6, SNP: 203, Loss: 21.9942, OOB Acc: 57.69%, # of Haplo: 28
7, SNP: 130, Loss: 18.122, OOB Acc: 61.54%, # of Haplo: 36
8, SNP: 14, Loss: 12.615, OOB Acc: 61.54%, # of Haplo: 38
9, SNP: 17, Loss: 13.5634, OOB Acc: 65.38%, # of Haplo: 41
10, SNP: 122, Loss: 12.8134, OOB Acc: 65.38%, # of Haplo: 41
11, SNP: 199, Loss: 12.3319, OOB Acc: 65.38%, # of Haplo: 41
12, SNP: 134, Loss: 11.1247, OOB Acc: 65.38%, # of Haplo: 41
13, SNP: 128, Loss: 10.7586, OOB Acc: 65.38%, # of Haplo: 41
14, SNP: 223, Loss: 10.3119, OOB Acc: 65.38%, # of Haplo: 42
15, SNP: 37, Loss: 10.0841, OOB Acc: 65.38%, # of Haplo: 44
16, SNP: 185, Loss: 9.84428, OOB Acc: 65.38%, # of Haplo: 44
17, SNP: 151, Loss: 9.21691, OOB Acc: 65.38%, # of Haplo: 44
18, SNP: 73, Loss: 7.59606, OOB Acc: 65.38%, # of Haplo: 46
19, SNP: 127, Loss: 7.59606, OOB Acc: 69.23%, # of Haplo: 46
20, SNP: 110, Loss: 7.59606, OOB Acc: 73.08%, # of Haplo: 46
21, SNP: 54, Loss: 9.02042, OOB Acc: 76.92%, # of Haplo: 52
22, SNP: 184, Loss: 7.59607, OOB Acc: 76.92%, # of Haplo: 52
[2] 2019-04-13 03:04:09, OOB Acc: 76.92%, # of SNPs: 22, # of Haplo: 52
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.703386e-13 1.713083e-09 1.712840e-08 2.197928e-03 5.839109e-03 1.241815e-02
Max. Mean SD
5.871033e-01 4.193081e-02 1.385420e-01
Accuracy with training data: 91.2%
Out-of-bag accuracy: 74.6%
HIBAG model: 2 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting by voting from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:04:09) 0%
Predicting (2019-04-13 03:04:09) 100%
HIBAG model: 2 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting by voting from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:04:09) 0%
Predicting (2019-04-13 03:04:09) 100%
Exclude 1 monomorphic SNP
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 13
# of SNPs: 158, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:04:10
1, SNP: 141, Loss: 378.06, OOB Acc: 52.08%, # of Haplo: 14
2, SNP: 74, Loss: 262.497, OOB Acc: 58.33%, # of Haplo: 15
3, SNP: 78, Loss: 162.497, OOB Acc: 68.75%, # of Haplo: 19
4, SNP: 118, Loss: 70.0426, OOB Acc: 72.92%, # of Haplo: 23
5, SNP: 82, Loss: 45.8279, OOB Acc: 83.33%, # of Haplo: 23
6, SNP: 95, Loss: 35.4414, OOB Acc: 89.58%, # of Haplo: 27
7, SNP: 89, Loss: 32.6134, OOB Acc: 89.58%, # of Haplo: 35
8, SNP: 83, Loss: 31.7921, OOB Acc: 89.58%, # of Haplo: 51
9, SNP: 151, Loss: 31.0653, OOB Acc: 89.58%, # of Haplo: 55
10, SNP: 94, Loss: 31.0246, OOB Acc: 89.58%, # of Haplo: 55
11, SNP: 111, Loss: 18.9027, OOB Acc: 89.58%, # of Haplo: 56
12, SNP: 139, Loss: 18.4248, OOB Acc: 89.58%, # of Haplo: 59
13, SNP: 93, Loss: 17.0195, OOB Acc: 91.67%, # of Haplo: 58
14, SNP: 15, Loss: 14.1692, OOB Acc: 91.67%, # of Haplo: 60
[1] 2019-04-13 03:04:10, OOB Acc: 91.67%, # of SNPs: 14, # of Haplo: 60
1, SNP: 94, Loss: 403.365, OOB Acc: 63.16%, # of Haplo: 15
2, SNP: 82, Loss: 294.053, OOB Acc: 71.05%, # of Haplo: 18
3, SNP: 57, Loss: 226.142, OOB Acc: 76.32%, # of Haplo: 23
4, SNP: 155, Loss: 197.199, OOB Acc: 84.21%, # of Haplo: 29
5, SNP: 44, Loss: 132.804, OOB Acc: 86.84%, # of Haplo: 40
6, SNP: 30, Loss: 122.507, OOB Acc: 92.11%, # of Haplo: 40
7, SNP: 109, Loss: 72.0179, OOB Acc: 92.11%, # of Haplo: 41
8, SNP: 72, Loss: 59.3281, OOB Acc: 92.11%, # of Haplo: 41
9, SNP: 36, Loss: 54.939, OOB Acc: 94.74%, # of Haplo: 43
10, SNP: 127, Loss: 48.1392, OOB Acc: 94.74%, # of Haplo: 43
11, SNP: 53, Loss: 44.7676, OOB Acc: 94.74%, # of Haplo: 43
12, SNP: 148, Loss: 43.047, OOB Acc: 94.74%, # of Haplo: 44
13, SNP: 152, Loss: 40.2104, OOB Acc: 94.74%, # of Haplo: 45
14, SNP: 125, Loss: 39.8862, OOB Acc: 94.74%, # of Haplo: 45
15, SNP: 78, Loss: 39.5652, OOB Acc: 94.74%, # of Haplo: 45
16, SNP: 3, Loss: 39.0621, OOB Acc: 94.74%, # of Haplo: 47
17, SNP: 141, Loss: 37.6822, OOB Acc: 94.74%, # of Haplo: 47
18, SNP: 1, Loss: 36.5022, OOB Acc: 94.74%, # of Haplo: 50
[2] 2019-04-13 03:04:10, OOB Acc: 94.74%, # of SNPs: 18, # of Haplo: 50
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02
Max. Mean SD
5.948644e-01 6.324378e-02 1.404915e-01
Accuracy with training data: 96.7%
Out-of-bag accuracy: 93.2%
Gene: A
Training dataset: 60 samples X 158 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 28
avg. # of SNPs in an individual classifier: 16.00
(sd: 2.83, min: 14, max: 18, median: 16.00)
avg. # of haplotypes in an individual classifier: 55.00
(sd: 7.07, min: 50, max: 60, median: 55.00)
avg. out-of-bag accuracy: 93.20%
(sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02
Max. Mean SD
5.948644e-01 6.324378e-02 1.404915e-01
Genome assembly: hg19
Remove 130 unused SNPs.
Gene: A
Training dataset: 60 samples X 28 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 28
avg. # of SNPs in an individual classifier: 16.00
(sd: 2.83, min: 14, max: 18, median: 16.00)
avg. # of haplotypes in an individual classifier: 55.00
(sd: 7.07, min: 50, max: 60, median: 55.00)
avg. out-of-bag accuracy: 93.20%
(sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02
Max. Mean SD
5.948644e-01 6.324378e-02 1.404915e-01
Genome assembly: hg19
Platform: Illumina 1M Duo
Information: Training set -- HapMap Phase II
HIBAG model: 2 individual classifiers, 158 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 60
Predicting (2019-04-13 03:04:10) 0%
Predicting (2019-04-13 03:04:10) 100%
HIBAG model: 2 individual classifiers, 28 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 60
Predicting (2019-04-13 03:04:10) 0%
Predicting (2019-04-13 03:04:10) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:04:10
1, SNP: 123, Loss: 203.122, OOB Acc: 54.55%, # of Haplo: 14
2, SNP: 182, Loss: 155.176, OOB Acc: 63.64%, # of Haplo: 16
3, SNP: 106, Loss: 123.308, OOB Acc: 77.27%, # of Haplo: 16
4, SNP: 87, Loss: 77.8709, OOB Acc: 81.82%, # of Haplo: 20
5, SNP: 114, Loss: 43.8425, OOB Acc: 90.91%, # of Haplo: 20
6, SNP: 7, Loss: 36.5344, OOB Acc: 90.91%, # of Haplo: 23
7, SNP: 199, Loss: 26.705, OOB Acc: 90.91%, # of Haplo: 24
8, SNP: 201, Loss: 16.5421, OOB Acc: 90.91%, # of Haplo: 26
9, SNP: 102, Loss: 15.7295, OOB Acc: 90.91%, # of Haplo: 33
10, SNP: 144, Loss: 15.078, OOB Acc: 90.91%, # of Haplo: 33
11, SNP: 52, Loss: 14.8361, OOB Acc: 90.91%, # of Haplo: 42
12, SNP: 108, Loss: 14.7598, OOB Acc: 90.91%, # of Haplo: 42
13, SNP: 73, Loss: 11.7396, OOB Acc: 90.91%, # of Haplo: 43
14, SNP: 151, Loss: 11.3074, OOB Acc: 90.91%, # of Haplo: 45
[1] 2019-04-13 03:04:10, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 45
1, SNP: 45, Loss: 180.067, OOB Acc: 50.00%, # of Haplo: 15
2, SNP: 75, Loss: 135.995, OOB Acc: 60.71%, # of Haplo: 18
3, SNP: 176, Loss: 100.071, OOB Acc: 64.29%, # of Haplo: 24
4, SNP: 181, Loss: 72.877, OOB Acc: 67.86%, # of Haplo: 34
5, SNP: 154, Loss: 60.9088, OOB Acc: 71.43%, # of Haplo: 35
6, SNP: 180, Loss: 56.0063, OOB Acc: 71.43%, # of Haplo: 39
7, SNP: 164, Loss: 51.6779, OOB Acc: 71.43%, # of Haplo: 48
8, SNP: 227, Loss: 47.3381, OOB Acc: 71.43%, # of Haplo: 50
9, SNP: 197, Loss: 40.1255, OOB Acc: 71.43%, # of Haplo: 50
10, SNP: 165, Loss: 38.8164, OOB Acc: 71.43%, # of Haplo: 49
11, SNP: 151, Loss: 34.5811, OOB Acc: 71.43%, # of Haplo: 49
12, SNP: 222, Loss: 34.3146, OOB Acc: 71.43%, # of Haplo: 53
13, SNP: 111, Loss: 34.3145, OOB Acc: 75.00%, # of Haplo: 61
14, SNP: 73, Loss: 27.4168, OOB Acc: 75.00%, # of Haplo: 61
15, SNP: 132, Loss: 27.3856, OOB Acc: 75.00%, # of Haplo: 61
16, SNP: 199, Loss: 25.7831, OOB Acc: 75.00%, # of Haplo: 61
[2] 2019-04-13 03:04:10, OOB Acc: 75.00%, # of SNPs: 16, # of Haplo: 61
1, SNP: 119, Loss: 233.718, OOB Acc: 50.00%, # of Haplo: 13
2, SNP: 232, Loss: 181.18, OOB Acc: 52.78%, # of Haplo: 16
3, SNP: 71, Loss: 139.471, OOB Acc: 58.33%, # of Haplo: 18
4, SNP: 166, Loss: 116.525, OOB Acc: 61.11%, # of Haplo: 20
5, SNP: 116, Loss: 87.4554, OOB Acc: 63.89%, # of Haplo: 24
6, SNP: 126, Loss: 42.9667, OOB Acc: 66.67%, # of Haplo: 30
7, SNP: 33, Loss: 32.6021, OOB Acc: 66.67%, # of Haplo: 41
8, SNP: 102, Loss: 30.9058, OOB Acc: 69.44%, # of Haplo: 41
9, SNP: 169, Loss: 30.4075, OOB Acc: 72.22%, # of Haplo: 41
10, SNP: 148, Loss: 29.993, OOB Acc: 72.22%, # of Haplo: 41
11, SNP: 87, Loss: 29.5166, OOB Acc: 72.22%, # of Haplo: 41
12, SNP: 70, Loss: 24.11, OOB Acc: 72.22%, # of Haplo: 41
13, SNP: 151, Loss: 23.5686, OOB Acc: 72.22%, # of Haplo: 42
[3] 2019-04-13 03:04:10, OOB Acc: 72.22%, # of SNPs: 13, # of Haplo: 42
1, SNP: 102, Loss: 142.289, OOB Acc: 38.46%, # of Haplo: 15
2, SNP: 75, Loss: 110.14, OOB Acc: 46.15%, # of Haplo: 19
3, SNP: 214, Loss: 91.7293, OOB Acc: 53.85%, # of Haplo: 19
4, SNP: 121, Loss: 63.0626, OOB Acc: 57.69%, # of Haplo: 19
5, SNP: 194, Loss: 34.2678, OOB Acc: 57.69%, # of Haplo: 23
6, SNP: 203, Loss: 21.9942, OOB Acc: 57.69%, # of Haplo: 28
7, SNP: 130, Loss: 18.122, OOB Acc: 61.54%, # of Haplo: 36
8, SNP: 14, Loss: 12.615, OOB Acc: 61.54%, # of Haplo: 38
9, SNP: 17, Loss: 13.5634, OOB Acc: 65.38%, # of Haplo: 41
10, SNP: 122, Loss: 12.8134, OOB Acc: 65.38%, # of Haplo: 41
11, SNP: 199, Loss: 12.3319, OOB Acc: 65.38%, # of Haplo: 41
12, SNP: 134, Loss: 11.1247, OOB Acc: 65.38%, # of Haplo: 41
13, SNP: 128, Loss: 10.7586, OOB Acc: 65.38%, # of Haplo: 41
14, SNP: 223, Loss: 10.3119, OOB Acc: 65.38%, # of Haplo: 42
15, SNP: 37, Loss: 10.0841, OOB Acc: 65.38%, # of Haplo: 44
16, SNP: 185, Loss: 9.84428, OOB Acc: 65.38%, # of Haplo: 44
17, SNP: 151, Loss: 9.21691, OOB Acc: 65.38%, # of Haplo: 44
18, SNP: 73, Loss: 7.59606, OOB Acc: 65.38%, # of Haplo: 46
19, SNP: 127, Loss: 7.59606, OOB Acc: 69.23%, # of Haplo: 46
20, SNP: 110, Loss: 7.59606, OOB Acc: 73.08%, # of Haplo: 46
21, SNP: 54, Loss: 9.02042, OOB Acc: 76.92%, # of Haplo: 52
22, SNP: 184, Loss: 7.59607, OOB Acc: 76.92%, # of Haplo: 52
[4] 2019-04-13 03:04:10, OOB Acc: 76.92%, # of SNPs: 22, # of Haplo: 52
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162630 0.0002210881 0.0002645141 0.0039247319 0.0075458074 0.0145272758
Max. Mean SD
0.4532643335 0.0352737673 0.1063114572
Accuracy with training data: 94.1%
Out-of-bag accuracy: 78.8%
Gene: A
Training dataset: 34 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 54
avg. # of SNPs in an individual classifier: 16.25
(sd: 4.03, min: 13, max: 22, median: 15.00)
avg. # of haplotypes in an individual classifier: 50.00
(sd: 8.45, min: 42, max: 61, median: 48.50)
avg. out-of-bag accuracy: 78.76%
(sd: 8.32%, min: 72.22%, max: 90.91%, median: 75.96%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162630 0.0002210881 0.0002645141 0.0039247319 0.0075458074 0.0145272758
Max. Mean SD
0.4532643335 0.0352737673 0.1063114572
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:04:10) 0%
Predicting (2019-04-13 03:04:10) 100%
Allele Num. Freq. Num. Freq. CR ACC SEN SPE PPV NPV Miscall
Train Train Valid. Valid. (%) (%) (%) (%) (%) (%) (%)
----
Overall accuracy: 94.2%, Call rate: 100.0%
01:01 13 0.1912 12 0.2308 100.0 100.0 100.0 100.0 100.0 100.0 --
02:01 23 0.3382 20 0.3846 100.0 100.0 100.0 100.0 100.0 100.0 --
02:06 1 0.0147 0 0 -- -- -- -- -- -- --
03:01 4 0.0588 5 0.0962 100.0 100.0 100.0 100.0 100.0 100.0 --
11:01 3 0.0441 2 0.0385 100.0 98.1 100.0 98.0 66.7 100.0 --
23:01 2 0.0294 1 0.0192 100.0 98.1 0.0 100.0 -- 98.1 24:02 (100)
24:02 6 0.0882 5 0.0962 100.0 98.1 100.0 97.9 83.3 100.0 --
24:03 1 0.0147 0 0 -- -- -- -- -- -- --
25:01 3 0.0441 2 0.0385 100.0 98.1 50.0 100.0 100.0 98.0 26:01 (100)
26:01 2 0.0294 1 0.0192 100.0 98.1 100.0 98.0 50.0 100.0 --
29:02 3 0.0441 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
31:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
32:01 3 0.0441 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
68:01 2 0.0294 1 0.0192 100.0 98.1 0.0 100.0 -- 98.1 11:01 (100)
\title{Imputation Evaluation}
\documentclass[12pt]{article}
\usepackage{fullpage}
\usepackage{longtable}
\begin{document}
\maketitle
\setlength{\LTcapwidth}{6.5in}
% -------- BEGIN TABLE --------
\begin{longtable}{rrrrr | rrrrrrl}
\caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).}
\label{tab:accuracy} \\
Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endfirsthead
\multicolumn{12}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\
Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endhead
\hline
\multicolumn{12}{r}{Continued on next page ...} \\
\hline
\endfoot
\hline\hline
\endlastfoot
\multicolumn{12}{l}{\it Overall accuracy: 94.2\%, Call rate: 100.0\%} \\
01:01 & 13 & 0.1912 & 12 & 0.2308 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
02:01 & 23 & 0.3382 & 20 & 0.3846 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
02:06 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
03:01 & 4 & 0.0588 & 5 & 0.0962 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
11:01 & 3 & 0.0441 & 2 & 0.0385 & 100.0 & 98.1 & 100.0 & 98.0 & 66.7 & 100.0 & -- \\
23:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 98.1 & 0.0 & 100.0 & -- & 98.1 & 24:02 (100) \\
24:02 & 6 & 0.0882 & 5 & 0.0962 & 100.0 & 98.1 & 100.0 & 97.9 & 83.3 & 100.0 & -- \\
24:03 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
25:01 & 3 & 0.0441 & 2 & 0.0385 & 100.0 & 98.1 & 50.0 & 100.0 & 100.0 & 98.0 & 26:01 (100) \\
26:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 98.1 & 100.0 & 98.0 & 50.0 & 100.0 & -- \\
29:02 & 3 & 0.0441 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
31:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
32:01 & 3 & 0.0441 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
68:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 98.1 & 0.0 & 100.0 & -- & 98.1 & 11:01 (100) \\
\end{longtable}
% -------- END TABLE --------
\end{document}
<!DOCTYPE html>
<html>
<head>
<title>Imputation Evaluation</title>
</head>
<body>
<h1>Imputation Evaluation</h1>
<p></p>
<h3><b>Table 1L:</b> The sensitivity (SEN), specificity (SPE),
positive predictive value (PPV), negative predictive value (NPV)
and call rate (CR).</h3>
<table id="TB-Acc" class="tabular" border="1" CELLSPACING="1">
<tr>
<th>Allele </th> <th>Num. Train</th> <th>Freq. Train</th> <th>Num. Valid.</th> <th>Freq. Valid.</th> <th>CR (%)</th> <th>ACC (%)</th> <th>SEN (%)</th> <th>SPE (%)</th> <th>PPV (%)</th> <th>NPV (%)</th> <th>Miscall (%)</th>
</tr>
<tr>
<td colspan="12">
<i> Overall accuracy: 94.2%, Call rate: 100.0% </i>
</td>
</tr>
<tr>
<td>01:01</td> <td>13</td> <td>0.1912</td> <td>12</td> <td>0.2308</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:01</td> <td>23</td> <td>0.3382</td> <td>20</td> <td>0.3846</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:06</td> <td>1</td> <td>0.0147</td> <td>0</td> <td>0</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td>
</tr>
<tr>
<td>03:01</td> <td>4</td> <td>0.0588</td> <td>5</td> <td>0.0962</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>11:01</td> <td>3</td> <td>0.0441</td> <td>2</td> <td>0.0385</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>98.0</td> <td>66.7</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>23:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>98.1</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>24:02</td> <td>6</td> <td>0.0882</td> <td>5</td> <td>0.0962</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>97.9</td> <td>83.3</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>24:03</td> <td>1</td> <td>0.0147</td> <td>0</td> <td>0</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td>
</tr>
<tr>
<td>25:01</td> <td>3</td> <td>0.0441</td> <td>2</td> <td>0.0385</td> <td>100.0</td> <td>98.1</td> <td>50.0</td> <td>100.0</td> <td>100.0</td> <td>98.0</td> <td>26:01 (100)</td>
</tr>
<tr>
<td>26:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>98.0</td> <td>50.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>29:02</td> <td>3</td> <td>0.0441</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>31:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>32:01</td> <td>3</td> <td>0.0441</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>68:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>98.1</td> <td>11:01 (100)</td>
</tr>
</table>
</body>
</html>
**Overall accuracy: 94.2%, Call rate: 100.0%**
| Allele | # Train | Freq. Train | # Valid. | Freq. Valid. | CR (%) | ACC (%) | SEN (%) | SPE (%) | PPV (%) | NPV (%) | Miscall (%) |
|:--|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|:--|
| 01:01 | 13 | 0.1912 | 12 | 0.2308 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 02:01 | 23 | 0.3382 | 20 | 0.3846 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 02:06 | 1 | 0.0147 | 0 | 0 | -- | -- | -- | -- | -- | -- | -- |
| 03:01 | 4 | 0.0588 | 5 | 0.0962 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 11:01 | 3 | 0.0441 | 2 | 0.0385 | 100.0 | 98.1 | 100.0 | 98.0 | 66.7 | 100.0 | -- |
| 23:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 98.1 | 0.0 | 100.0 | -- | 98.1 | 24:02 (100) |
| 24:02 | 6 | 0.0882 | 5 | 0.0962 | 100.0 | 98.1 | 100.0 | 97.9 | 83.3 | 100.0 | -- |
| 24:03 | 1 | 0.0147 | 0 | 0 | -- | -- | -- | -- | -- | -- | -- |
| 25:01 | 3 | 0.0441 | 2 | 0.0385 | 100.0 | 98.1 | 50.0 | 100.0 | 100.0 | 98.0 | 26:01 (100) |
| 26:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 98.1 | 100.0 | 98.0 | 50.0 | 100.0 | -- |
| 29:02 | 3 | 0.0441 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 31:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 32:01 | 3 | 0.0441 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 68:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 98.1 | 0.0 | 100.0 | -- | 98.1 | 11:01 (100) |
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:04:10
1, SNP: 123, Loss: 203.122, OOB Acc: 54.55%, # of Haplo: 14
2, SNP: 182, Loss: 155.176, OOB Acc: 63.64%, # of Haplo: 16
3, SNP: 106, Loss: 123.308, OOB Acc: 77.27%, # of Haplo: 16
4, SNP: 87, Loss: 77.8709, OOB Acc: 81.82%, # of Haplo: 20
5, SNP: 114, Loss: 43.8425, OOB Acc: 90.91%, # of Haplo: 20
6, SNP: 7, Loss: 36.5344, OOB Acc: 90.91%, # of Haplo: 23
7, SNP: 199, Loss: 26.705, OOB Acc: 90.91%, # of Haplo: 24
8, SNP: 201, Loss: 16.5421, OOB Acc: 90.91%, # of Haplo: 26
9, SNP: 102, Loss: 15.7295, OOB Acc: 90.91%, # of Haplo: 33
10, SNP: 144, Loss: 15.078, OOB Acc: 90.91%, # of Haplo: 33
11, SNP: 52, Loss: 14.8361, OOB Acc: 90.91%, # of Haplo: 42
12, SNP: 108, Loss: 14.7598, OOB Acc: 90.91%, # of Haplo: 42
13, SNP: 73, Loss: 11.7396, OOB Acc: 90.91%, # of Haplo: 43
14, SNP: 151, Loss: 11.3074, OOB Acc: 90.91%, # of Haplo: 45
[1] 2019-04-13 03:04:11, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 45
1, SNP: 45, Loss: 180.067, OOB Acc: 50.00%, # of Haplo: 15
2, SNP: 75, Loss: 135.995, OOB Acc: 60.71%, # of Haplo: 18
3, SNP: 176, Loss: 100.071, OOB Acc: 64.29%, # of Haplo: 24
4, SNP: 181, Loss: 72.877, OOB Acc: 67.86%, # of Haplo: 34
5, SNP: 154, Loss: 60.9088, OOB Acc: 71.43%, # of Haplo: 35
6, SNP: 180, Loss: 56.0063, OOB Acc: 71.43%, # of Haplo: 39
7, SNP: 164, Loss: 51.6779, OOB Acc: 71.43%, # of Haplo: 48
8, SNP: 227, Loss: 47.3381, OOB Acc: 71.43%, # of Haplo: 50
9, SNP: 197, Loss: 40.1255, OOB Acc: 71.43%, # of Haplo: 50
10, SNP: 165, Loss: 38.8164, OOB Acc: 71.43%, # of Haplo: 49
11, SNP: 151, Loss: 34.5811, OOB Acc: 71.43%, # of Haplo: 49
12, SNP: 222, Loss: 34.3146, OOB Acc: 71.43%, # of Haplo: 53
13, SNP: 111, Loss: 34.3145, OOB Acc: 75.00%, # of Haplo: 61
14, SNP: 73, Loss: 27.4168, OOB Acc: 75.00%, # of Haplo: 61
15, SNP: 132, Loss: 27.3856, OOB Acc: 75.00%, # of Haplo: 61
16, SNP: 199, Loss: 25.7831, OOB Acc: 75.00%, # of Haplo: 61
[2] 2019-04-13 03:04:11, OOB Acc: 75.00%, # of SNPs: 16, # of Haplo: 61
1, SNP: 119, Loss: 233.718, OOB Acc: 50.00%, # of Haplo: 13
2, SNP: 232, Loss: 181.18, OOB Acc: 52.78%, # of Haplo: 16
3, SNP: 71, Loss: 139.471, OOB Acc: 58.33%, # of Haplo: 18
4, SNP: 166, Loss: 116.525, OOB Acc: 61.11%, # of Haplo: 20
5, SNP: 116, Loss: 87.4554, OOB Acc: 63.89%, # of Haplo: 24
6, SNP: 126, Loss: 42.9667, OOB Acc: 66.67%, # of Haplo: 30
7, SNP: 33, Loss: 32.6021, OOB Acc: 66.67%, # of Haplo: 41
8, SNP: 102, Loss: 30.9058, OOB Acc: 69.44%, # of Haplo: 41
9, SNP: 169, Loss: 30.4075, OOB Acc: 72.22%, # of Haplo: 41
10, SNP: 148, Loss: 29.993, OOB Acc: 72.22%, # of Haplo: 41
11, SNP: 87, Loss: 29.5166, OOB Acc: 72.22%, # of Haplo: 41
12, SNP: 70, Loss: 24.11, OOB Acc: 72.22%, # of Haplo: 41
13, SNP: 151, Loss: 23.5686, OOB Acc: 72.22%, # of Haplo: 42
[3] 2019-04-13 03:04:11, OOB Acc: 72.22%, # of SNPs: 13, # of Haplo: 42
1, SNP: 102, Loss: 142.289, OOB Acc: 38.46%, # of Haplo: 15
2, SNP: 75, Loss: 110.14, OOB Acc: 46.15%, # of Haplo: 19
3, SNP: 214, Loss: 91.7293, OOB Acc: 53.85%, # of Haplo: 19
4, SNP: 121, Loss: 63.0626, OOB Acc: 57.69%, # of Haplo: 19
5, SNP: 194, Loss: 34.2678, OOB Acc: 57.69%, # of Haplo: 23
6, SNP: 203, Loss: 21.9942, OOB Acc: 57.69%, # of Haplo: 28
7, SNP: 130, Loss: 18.122, OOB Acc: 61.54%, # of Haplo: 36
8, SNP: 14, Loss: 12.615, OOB Acc: 61.54%, # of Haplo: 38
9, SNP: 17, Loss: 13.5634, OOB Acc: 65.38%, # of Haplo: 41
10, SNP: 122, Loss: 12.8134, OOB Acc: 65.38%, # of Haplo: 41
11, SNP: 199, Loss: 12.3319, OOB Acc: 65.38%, # of Haplo: 41
12, SNP: 134, Loss: 11.1247, OOB Acc: 65.38%, # of Haplo: 41
13, SNP: 128, Loss: 10.7586, OOB Acc: 65.38%, # of Haplo: 41
14, SNP: 223, Loss: 10.3119, OOB Acc: 65.38%, # of Haplo: 42
15, SNP: 37, Loss: 10.0841, OOB Acc: 65.38%, # of Haplo: 44
16, SNP: 185, Loss: 9.84428, OOB Acc: 65.38%, # of Haplo: 44
17, SNP: 151, Loss: 9.21691, OOB Acc: 65.38%, # of Haplo: 44
18, SNP: 73, Loss: 7.59606, OOB Acc: 65.38%, # of Haplo: 46
19, SNP: 127, Loss: 7.59606, OOB Acc: 69.23%, # of Haplo: 46
20, SNP: 110, Loss: 7.59606, OOB Acc: 73.08%, # of Haplo: 46
21, SNP: 54, Loss: 9.02042, OOB Acc: 76.92%, # of Haplo: 52
22, SNP: 184, Loss: 7.59607, OOB Acc: 76.92%, # of Haplo: 52
[4] 2019-04-13 03:04:11, OOB Acc: 76.92%, # of SNPs: 22, # of Haplo: 52
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162630 0.0002210881 0.0002645141 0.0039247319 0.0075458074 0.0145272758
Max. Mean SD
0.4532643335 0.0352737673 0.1063114572
Accuracy with training data: 94.1%
Out-of-bag accuracy: 78.8%
Gene: A
Training dataset: 34 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 54
avg. # of SNPs in an individual classifier: 16.25
(sd: 4.03, min: 13, max: 22, median: 15.00)
avg. # of haplotypes in an individual classifier: 50.00
(sd: 8.45, min: 42, max: 61, median: 48.50)
avg. out-of-bag accuracy: 78.76%
(sd: 8.32%, min: 72.22%, max: 90.91%, median: 75.96%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162630 0.0002210881 0.0002645141 0.0039247319 0.0075458074 0.0145272758
Max. Mean SD
0.4532643335 0.0352737673 0.1063114572
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:04:11) 0%
Predicting (2019-04-13 03:04:11) 100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 8
# of SNPs: 51, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:04:11
1, SNP: 13, Loss: 391.274, OOB Acc: 41.67%, # of Haplo: 17
2, SNP: 2, Loss: 321.685, OOB Acc: 52.08%, # of Haplo: 18
3, SNP: 36, Loss: 232.846, OOB Acc: 58.33%, # of Haplo: 19
4, SNP: 28, Loss: 178.077, OOB Acc: 62.50%, # of Haplo: 20
5, SNP: 35, Loss: 107.151, OOB Acc: 68.75%, # of Haplo: 20
6, SNP: 3, Loss: 72.2736, OOB Acc: 72.92%, # of Haplo: 23
7, SNP: 19, Loss: 50.8439, OOB Acc: 77.08%, # of Haplo: 25
8, SNP: 4, Loss: 47.2744, OOB Acc: 83.33%, # of Haplo: 29
9, SNP: 42, Loss: 47.0092, OOB Acc: 85.42%, # of Haplo: 37
10, SNP: 33, Loss: 41.5486, OOB Acc: 85.42%, # of Haplo: 41
11, SNP: 5, Loss: 39.769, OOB Acc: 85.42%, # of Haplo: 51
12, SNP: 10, Loss: 34.0977, OOB Acc: 85.42%, # of Haplo: 51
13, SNP: 37, Loss: 32.3969, OOB Acc: 85.42%, # of Haplo: 52
14, SNP: 7, Loss: 28.1492, OOB Acc: 85.42%, # of Haplo: 52
15, SNP: 15, Loss: 27.2163, OOB Acc: 85.42%, # of Haplo: 55
[1] 2019-04-13 03:04:11, OOB Acc: 85.42%, # of SNPs: 15, # of Haplo: 55
1, SNP: 18, Loss: 453.852, OOB Acc: 44.12%, # of Haplo: 17
2, SNP: 4, Loss: 358.517, OOB Acc: 50.00%, # of Haplo: 18
3, SNP: 49, Loss: 258.495, OOB Acc: 52.94%, # of Haplo: 18
4, SNP: 5, Loss: 172.555, OOB Acc: 67.65%, # of Haplo: 21
5, SNP: 42, Loss: 144.905, OOB Acc: 76.47%, # of Haplo: 21
6, SNP: 38, Loss: 98.7462, OOB Acc: 79.41%, # of Haplo: 21
7, SNP: 36, Loss: 83.4743, OOB Acc: 82.35%, # of Haplo: 24
8, SNP: 19, Loss: 60.2385, OOB Acc: 88.24%, # of Haplo: 24
9, SNP: 46, Loss: 49.1775, OOB Acc: 88.24%, # of Haplo: 24
10, SNP: 20, Loss: 42.3205, OOB Acc: 88.24%, # of Haplo: 24
11, SNP: 12, Loss: 41.1299, OOB Acc: 91.18%, # of Haplo: 25
12, SNP: 1, Loss: 33.8332, OOB Acc: 91.18%, # of Haplo: 25
13, SNP: 37, Loss: 32.8313, OOB Acc: 91.18%, # of Haplo: 26
14, SNP: 7, Loss: 38.8398, OOB Acc: 94.12%, # of Haplo: 27
15, SNP: 15, Loss: 35.0817, OOB Acc: 94.12%, # of Haplo: 32
16, SNP: 39, Loss: 33.7063, OOB Acc: 94.12%, # of Haplo: 30
[2] 2019-04-13 03:04:11, OOB Acc: 94.12%, # of SNPs: 16, # of Haplo: 30
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
3.479253e-10 4.700783e-10 1.569456e-09 3.197938e-03 1.266674e-02 3.773631e-02
Max. Mean SD
9.805734e-02 2.430678e-02 2.697855e-02
Accuracy with training data: 95.8%
Out-of-bag accuracy: 89.8%
Gene: C
Training dataset: 60 samples X 51 SNPs
# of HLA alleles: 17
# of individual classifiers: 2
total # of SNPs used: 23
avg. # of SNPs in an individual classifier: 15.50
(sd: 0.71, min: 15, max: 16, median: 15.50)
avg. # of haplotypes in an individual classifier: 42.50
(sd: 17.68, min: 30, max: 55, median: 42.50)
avg. out-of-bag accuracy: 89.77%
(sd: 6.15%, min: 85.42%, max: 94.12%, median: 89.77%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
3.479253e-10 4.700783e-10 1.569456e-09 3.197938e-03 1.266674e-02 3.773631e-02
Max. Mean SD
9.805734e-02 2.430678e-02 2.697855e-02
Genome assembly: hg19
Gene: C
Training dataset: 60 samples X 51 SNPs
# of HLA alleles: 17
# of individual classifiers: 1
total # of SNPs used: 15
avg. # of SNPs in an individual classifier: 15.00
(sd: NA, min: 15, max: 15, median: 15.00)
avg. # of haplotypes in an individual classifier: 55.00
(sd: NA, min: 55, max: 55, median: 55.00)
avg. out-of-bag accuracy: 85.42%
(sd: NA%, min: 85.42%, max: 85.42%, median: 85.42%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
3.479253e-10 4.700783e-10 1.569456e-09 3.197938e-03 1.266674e-02 3.773631e-02
Max. Mean SD
9.805734e-02 2.430678e-02 2.697855e-02
Genome assembly: hg19
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:04:11
1, SNP: 44, Loss: 396.506, OOB Acc: 41.67%, # of Haplo: 17
2, SNP: 58, Loss: 291.148, OOB Acc: 50.00%, # of Haplo: 17
3, SNP: 80, Loss: 211.469, OOB Acc: 56.25%, # of Haplo: 20
4, SNP: 18, Loss: 138.615, OOB Acc: 60.42%, # of Haplo: 20
5, SNP: 29, Loss: 111.977, OOB Acc: 62.50%, # of Haplo: 22
6, SNP: 62, Loss: 90.976, OOB Acc: 68.75%, # of Haplo: 24
7, SNP: 13, Loss: 70.2962, OOB Acc: 72.92%, # of Haplo: 24
8, SNP: 14, Loss: 54.5685, OOB Acc: 77.08%, # of Haplo: 22
9, SNP: 72, Loss: 35.1951, OOB Acc: 77.08%, # of Haplo: 24
10, SNP: 3, Loss: 23.2868, OOB Acc: 79.17%, # of Haplo: 24
11, SNP: 70, Loss: 21.089, OOB Acc: 79.17%, # of Haplo: 28
12, SNP: 5, Loss: 20.9664, OOB Acc: 79.17%, # of Haplo: 29
13, SNP: 40, Loss: 20.9662, OOB Acc: 83.33%, # of Haplo: 37
14, SNP: 24, Loss: 20.2385, OOB Acc: 85.42%, # of Haplo: 38
15, SNP: 2, Loss: 20.2383, OOB Acc: 87.50%, # of Haplo: 39
16, SNP: 74, Loss: 20.0601, OOB Acc: 87.50%, # of Haplo: 40
17, SNP: 57, Loss: 17.8846, OOB Acc: 87.50%, # of Haplo: 41
18, SNP: 56, Loss: 14.7377, OOB Acc: 89.58%, # of Haplo: 43
19, SNP: 27, Loss: 10.7709, OOB Acc: 89.58%, # of Haplo: 43
[1] 2019-04-13 03:04:11, OOB Acc: 89.58%, # of SNPs: 19, # of Haplo: 43
1, SNP: 66, Loss: 434.369, OOB Acc: 44.12%, # of Haplo: 19
2, SNP: 28, Loss: 337.451, OOB Acc: 58.82%, # of Haplo: 21
3, SNP: 30, Loss: 302.194, OOB Acc: 73.53%, # of Haplo: 21
4, SNP: 59, Loss: 209.932, OOB Acc: 73.53%, # of Haplo: 21
5, SNP: 69, Loss: 146.631, OOB Acc: 82.35%, # of Haplo: 21
6, SNP: 73, Loss: 96.4111, OOB Acc: 91.18%, # of Haplo: 21
7, SNP: 70, Loss: 81.5466, OOB Acc: 91.18%, # of Haplo: 21
8, SNP: 3, Loss: 71.8294, OOB Acc: 91.18%, # of Haplo: 22
9, SNP: 79, Loss: 63.7214, OOB Acc: 94.12%, # of Haplo: 26
10, SNP: 5, Loss: 59.4963, OOB Acc: 94.12%, # of Haplo: 27
11, SNP: 27, Loss: 39.6101, OOB Acc: 94.12%, # of Haplo: 27
12, SNP: 15, Loss: 39.0565, OOB Acc: 94.12%, # of Haplo: 27
13, SNP: 56, Loss: 32.0611, OOB Acc: 94.12%, # of Haplo: 30
14, SNP: 2, Loss: 28.065, OOB Acc: 94.12%, # of Haplo: 33
15, SNP: 6, Loss: 26.52, OOB Acc: 94.12%, # of Haplo: 37
16, SNP: 81, Loss: 26.4461, OOB Acc: 94.12%, # of Haplo: 43
17, SNP: 82, Loss: 25.6218, OOB Acc: 94.12%, # of Haplo: 51
18, SNP: 32, Loss: 20.8498, OOB Acc: 94.12%, # of Haplo: 52
[2] 2019-04-13 03:04:12, OOB Acc: 94.12%, # of SNPs: 18, # of Haplo: 52
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.361139e-08 7.194017e-06 7.172766e-05 1.579874e-03 9.379945e-03 3.397539e-02
Max. Mean SD
7.489335e-02 1.870792e-02 2.126647e-02
Accuracy with training data: 96.7%
Out-of-bag accuracy: 91.9%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:04:12
1, SNP: 123, Loss: 203.122, OOB Acc: 54.55%, # of Haplo: 14
2, SNP: 182, Loss: 155.176, OOB Acc: 63.64%, # of Haplo: 16
3, SNP: 106, Loss: 123.308, OOB Acc: 77.27%, # of Haplo: 16
4, SNP: 87, Loss: 77.8709, OOB Acc: 81.82%, # of Haplo: 20
5, SNP: 114, Loss: 43.8425, OOB Acc: 90.91%, # of Haplo: 20
6, SNP: 7, Loss: 36.5344, OOB Acc: 90.91%, # of Haplo: 23
7, SNP: 199, Loss: 26.705, OOB Acc: 90.91%, # of Haplo: 24
8, SNP: 201, Loss: 16.5421, OOB Acc: 90.91%, # of Haplo: 26
9, SNP: 102, Loss: 15.7295, OOB Acc: 90.91%, # of Haplo: 33
10, SNP: 144, Loss: 15.078, OOB Acc: 90.91%, # of Haplo: 33
11, SNP: 52, Loss: 14.8361, OOB Acc: 90.91%, # of Haplo: 42
12, SNP: 108, Loss: 14.7598, OOB Acc: 90.91%, # of Haplo: 42
13, SNP: 73, Loss: 11.7396, OOB Acc: 90.91%, # of Haplo: 43
14, SNP: 151, Loss: 11.3074, OOB Acc: 90.91%, # of Haplo: 45
[1] 2019-04-13 03:04:12, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 45
1, SNP: 45, Loss: 180.067, OOB Acc: 50.00%, # of Haplo: 15
2, SNP: 75, Loss: 135.995, OOB Acc: 60.71%, # of Haplo: 18
3, SNP: 176, Loss: 100.071, OOB Acc: 64.29%, # of Haplo: 24
4, SNP: 181, Loss: 72.877, OOB Acc: 67.86%, # of Haplo: 34
5, SNP: 154, Loss: 60.9088, OOB Acc: 71.43%, # of Haplo: 35
6, SNP: 180, Loss: 56.0063, OOB Acc: 71.43%, # of Haplo: 39
7, SNP: 164, Loss: 51.6779, OOB Acc: 71.43%, # of Haplo: 48
8, SNP: 227, Loss: 47.3381, OOB Acc: 71.43%, # of Haplo: 50
9, SNP: 197, Loss: 40.1255, OOB Acc: 71.43%, # of Haplo: 50
10, SNP: 165, Loss: 38.8164, OOB Acc: 71.43%, # of Haplo: 49
11, SNP: 151, Loss: 34.5811, OOB Acc: 71.43%, # of Haplo: 49
12, SNP: 222, Loss: 34.3146, OOB Acc: 71.43%, # of Haplo: 53
13, SNP: 111, Loss: 34.3145, OOB Acc: 75.00%, # of Haplo: 61
14, SNP: 73, Loss: 27.4168, OOB Acc: 75.00%, # of Haplo: 61
15, SNP: 132, Loss: 27.3856, OOB Acc: 75.00%, # of Haplo: 61
16, SNP: 199, Loss: 25.7831, OOB Acc: 75.00%, # of Haplo: 61
[2] 2019-04-13 03:04:12, OOB Acc: 75.00%, # of SNPs: 16, # of Haplo: 61
1, SNP: 119, Loss: 233.718, OOB Acc: 50.00%, # of Haplo: 13
2, SNP: 232, Loss: 181.18, OOB Acc: 52.78%, # of Haplo: 16
3, SNP: 71, Loss: 139.471, OOB Acc: 58.33%, # of Haplo: 18
4, SNP: 166, Loss: 116.525, OOB Acc: 61.11%, # of Haplo: 20
5, SNP: 116, Loss: 87.4554, OOB Acc: 63.89%, # of Haplo: 24
6, SNP: 126, Loss: 42.9667, OOB Acc: 66.67%, # of Haplo: 30
7, SNP: 33, Loss: 32.6021, OOB Acc: 66.67%, # of Haplo: 41
8, SNP: 102, Loss: 30.9058, OOB Acc: 69.44%, # of Haplo: 41
9, SNP: 169, Loss: 30.4075, OOB Acc: 72.22%, # of Haplo: 41
10, SNP: 148, Loss: 29.993, OOB Acc: 72.22%, # of Haplo: 41
11, SNP: 87, Loss: 29.5166, OOB Acc: 72.22%, # of Haplo: 41
12, SNP: 70, Loss: 24.11, OOB Acc: 72.22%, # of Haplo: 41
13, SNP: 151, Loss: 23.5686, OOB Acc: 72.22%, # of Haplo: 42
[3] 2019-04-13 03:04:12, OOB Acc: 72.22%, # of SNPs: 13, # of Haplo: 42
1, SNP: 102, Loss: 142.289, OOB Acc: 38.46%, # of Haplo: 15
2, SNP: 75, Loss: 110.14, OOB Acc: 46.15%, # of Haplo: 19
3, SNP: 214, Loss: 91.7293, OOB Acc: 53.85%, # of Haplo: 19
4, SNP: 121, Loss: 63.0626, OOB Acc: 57.69%, # of Haplo: 19
5, SNP: 194, Loss: 34.2678, OOB Acc: 57.69%, # of Haplo: 23
6, SNP: 203, Loss: 21.9942, OOB Acc: 57.69%, # of Haplo: 28
7, SNP: 130, Loss: 18.122, OOB Acc: 61.54%, # of Haplo: 36
8, SNP: 14, Loss: 12.615, OOB Acc: 61.54%, # of Haplo: 38
9, SNP: 17, Loss: 13.5634, OOB Acc: 65.38%, # of Haplo: 41
10, SNP: 122, Loss: 12.8134, OOB Acc: 65.38%, # of Haplo: 41
11, SNP: 199, Loss: 12.3319, OOB Acc: 65.38%, # of Haplo: 41
12, SNP: 134, Loss: 11.1247, OOB Acc: 65.38%, # of Haplo: 41
13, SNP: 128, Loss: 10.7586, OOB Acc: 65.38%, # of Haplo: 41
14, SNP: 223, Loss: 10.3119, OOB Acc: 65.38%, # of Haplo: 42
15, SNP: 37, Loss: 10.0841, OOB Acc: 65.38%, # of Haplo: 44
16, SNP: 185, Loss: 9.84428, OOB Acc: 65.38%, # of Haplo: 44
17, SNP: 151, Loss: 9.21691, OOB Acc: 65.38%, # of Haplo: 44
18, SNP: 73, Loss: 7.59606, OOB Acc: 65.38%, # of Haplo: 46
19, SNP: 127, Loss: 7.59606, OOB Acc: 69.23%, # of Haplo: 46
20, SNP: 110, Loss: 7.59606, OOB Acc: 73.08%, # of Haplo: 46
21, SNP: 54, Loss: 9.02042, OOB Acc: 76.92%, # of Haplo: 52
22, SNP: 184, Loss: 7.59607, OOB Acc: 76.92%, # of Haplo: 52
[4] 2019-04-13 03:04:12, OOB Acc: 76.92%, # of SNPs: 22, # of Haplo: 52
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162630 0.0002210881 0.0002645141 0.0039247319 0.0075458074 0.0145272758
Max. Mean SD
0.4532643335 0.0352737673 0.1063114572
Accuracy with training data: 94.1%
Out-of-bag accuracy: 78.8%
Gene: A
Training dataset: 34 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 54
avg. # of SNPs in an individual classifier: 16.25
(sd: 4.03, min: 13, max: 22, median: 15.00)
avg. # of haplotypes in an individual classifier: 50.00
(sd: 8.45, min: 42, max: 61, median: 48.50)
avg. out-of-bag accuracy: 78.76%
(sd: 8.32%, min: 72.22%, max: 90.91%, median: 75.96%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162630 0.0002210881 0.0002645141 0.0039247319 0.0075458074 0.0145272758
Max. Mean SD
0.4532643335 0.0352737673 0.1063114572
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:04:12) 0%
Predicting (2019-04-13 03:04:12) 100%
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:04:12
1, SNP: 44, Loss: 396.506, OOB Acc: 41.67%, # of Haplo: 17
2, SNP: 58, Loss: 291.148, OOB Acc: 50.00%, # of Haplo: 17
3, SNP: 80, Loss: 211.469, OOB Acc: 56.25%, # of Haplo: 20
4, SNP: 18, Loss: 138.615, OOB Acc: 60.42%, # of Haplo: 20
5, SNP: 29, Loss: 111.977, OOB Acc: 62.50%, # of Haplo: 22
6, SNP: 62, Loss: 90.976, OOB Acc: 68.75%, # of Haplo: 24
7, SNP: 13, Loss: 70.2962, OOB Acc: 72.92%, # of Haplo: 24
8, SNP: 14, Loss: 54.5685, OOB Acc: 77.08%, # of Haplo: 22
9, SNP: 72, Loss: 35.1951, OOB Acc: 77.08%, # of Haplo: 24
10, SNP: 3, Loss: 23.2868, OOB Acc: 79.17%, # of Haplo: 24
11, SNP: 70, Loss: 21.089, OOB Acc: 79.17%, # of Haplo: 28
12, SNP: 5, Loss: 20.9664, OOB Acc: 79.17%, # of Haplo: 29
13, SNP: 40, Loss: 20.9662, OOB Acc: 83.33%, # of Haplo: 37
14, SNP: 24, Loss: 20.2385, OOB Acc: 85.42%, # of Haplo: 38
15, SNP: 2, Loss: 20.2383, OOB Acc: 87.50%, # of Haplo: 39
16, SNP: 74, Loss: 20.0601, OOB Acc: 87.50%, # of Haplo: 40
17, SNP: 57, Loss: 17.8846, OOB Acc: 87.50%, # of Haplo: 41
18, SNP: 56, Loss: 14.7377, OOB Acc: 89.58%, # of Haplo: 43
19, SNP: 27, Loss: 10.7709, OOB Acc: 89.58%, # of Haplo: 43
[1] 2019-04-13 03:04:12, OOB Acc: 89.58%, # of SNPs: 19, # of Haplo: 43
1, SNP: 66, Loss: 434.369, OOB Acc: 44.12%, # of Haplo: 19
2, SNP: 28, Loss: 337.451, OOB Acc: 58.82%, # of Haplo: 21
3, SNP: 30, Loss: 302.194, OOB Acc: 73.53%, # of Haplo: 21
4, SNP: 59, Loss: 209.932, OOB Acc: 73.53%, # of Haplo: 21
5, SNP: 69, Loss: 146.631, OOB Acc: 82.35%, # of Haplo: 21
6, SNP: 73, Loss: 96.4111, OOB Acc: 91.18%, # of Haplo: 21
7, SNP: 70, Loss: 81.5466, OOB Acc: 91.18%, # of Haplo: 21
8, SNP: 3, Loss: 71.8294, OOB Acc: 91.18%, # of Haplo: 22
9, SNP: 79, Loss: 63.7214, OOB Acc: 94.12%, # of Haplo: 26
10, SNP: 5, Loss: 59.4963, OOB Acc: 94.12%, # of Haplo: 27
11, SNP: 27, Loss: 39.6101, OOB Acc: 94.12%, # of Haplo: 27
12, SNP: 15, Loss: 39.0565, OOB Acc: 94.12%, # of Haplo: 27
13, SNP: 56, Loss: 32.0611, OOB Acc: 94.12%, # of Haplo: 30
14, SNP: 2, Loss: 28.065, OOB Acc: 94.12%, # of Haplo: 33
15, SNP: 6, Loss: 26.52, OOB Acc: 94.12%, # of Haplo: 37
16, SNP: 81, Loss: 26.4461, OOB Acc: 94.12%, # of Haplo: 43
17, SNP: 82, Loss: 25.6218, OOB Acc: 94.12%, # of Haplo: 51
18, SNP: 32, Loss: 20.8498, OOB Acc: 94.12%, # of Haplo: 52
[2] 2019-04-13 03:04:12, OOB Acc: 94.12%, # of SNPs: 18, # of Haplo: 52
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.361139e-08 7.194017e-06 7.172766e-05 1.579874e-03 9.379945e-03 3.397539e-02
Max. Mean SD
7.489335e-02 1.870792e-02 2.126647e-02
Accuracy with training data: 96.7%
Out-of-bag accuracy: 91.9%
Gene: C
Training dataset: 60 samples X 83 SNPs
# of HLA alleles: 17
# of individual classifiers: 2
total # of SNPs used: 31
avg. # of SNPs in an individual classifier: 18.50
(sd: 0.71, min: 18, max: 19, median: 18.50)
avg. # of haplotypes in an individual classifier: 47.50
(sd: 6.36, min: 43, max: 52, median: 47.50)
avg. out-of-bag accuracy: 91.85%
(sd: 3.21%, min: 89.58%, max: 94.12%, median: 91.85%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.361139e-08 7.194017e-06 7.172766e-05 1.579874e-03 9.379945e-03 3.397539e-02
Max. Mean SD
7.489335e-02 1.870792e-02 2.126647e-02
Genome assembly: hg19
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
>
> proc.time()
user system elapsed
23.10 0.42 25.53
|
