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This page was generated on 2016-09-21 03:48:08 -0700 (Wed, 21 Sep 2016).
| Package 332/1257 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | |||||
| doppelgangR 1.1.2 Levi Waldron
| zin1 | Linux (Ubuntu 16.04 LTS) / x86_64 | NotNeeded | OK | ERROR | ||||||
| moscato1 | Windows Server 2008 R2 Standard (64-bit) / x64 | NotNeeded | OK | [ ERROR ] | OK | ||||||
| morelia | Mac OS X Mavericks (10.9.5) / x86_64 | NotNeeded | OK | ERROR | OK |
| Package: doppelgangR |
| Version: 1.1.2 |
| Command: rm -rf doppelgangR.buildbin-libdir doppelgangR.Rcheck && mkdir doppelgangR.buildbin-libdir doppelgangR.Rcheck && D:\biocbld\bbs-3.4-bioc\R\bin\R.exe CMD INSTALL --build --merge-multiarch --library=doppelgangR.buildbin-libdir doppelgangR_1.1.2.tar.gz >doppelgangR.Rcheck\00install.out 2>&1 && cp doppelgangR.Rcheck\00install.out doppelgangR-install.out && D:\biocbld\bbs-3.4-bioc\R\bin\R.exe CMD check --library=doppelgangR.buildbin-libdir --install="check:doppelgangR-install.out" --force-multiarch --no-vignettes --timings doppelgangR_1.1.2.tar.gz |
| StartedAt: 2016-09-20 07:21:30 -0700 (Tue, 20 Sep 2016) |
| EndedAt: 2016-09-20 07:30:23 -0700 (Tue, 20 Sep 2016) |
| EllapsedTime: 533.4 seconds |
| RetCode: 1 |
| Status: ERROR |
| CheckDir: doppelgangR.Rcheck |
| Warnings: NA |
##############################################################################
##############################################################################
###
### Running command:
###
### rm -rf doppelgangR.buildbin-libdir doppelgangR.Rcheck && mkdir doppelgangR.buildbin-libdir doppelgangR.Rcheck && D:\biocbld\bbs-3.4-bioc\R\bin\R.exe CMD INSTALL --build --merge-multiarch --library=doppelgangR.buildbin-libdir doppelgangR_1.1.2.tar.gz >doppelgangR.Rcheck\00install.out 2>&1 && cp doppelgangR.Rcheck\00install.out doppelgangR-install.out && D:\biocbld\bbs-3.4-bioc\R\bin\R.exe CMD check --library=doppelgangR.buildbin-libdir --install="check:doppelgangR-install.out" --force-multiarch --no-vignettes --timings doppelgangR_1.1.2.tar.gz
###
##############################################################################
##############################################################################
* using log directory 'D:/biocbld/bbs-3.4-bioc/meat/doppelgangR.Rcheck'
* using R version 3.3.1 (2016-06-21)
* using platform: x86_64-w64-mingw32 (64-bit)
* using session charset: ISO8859-1
* using option '--no-vignettes'
* checking for file 'doppelgangR/DESCRIPTION' ... OK
* this is package 'doppelgangR' version '1.1.2'
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'doppelgangR' can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking 'build' directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* loading checks for arch 'i386'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
** checking loading without being on the library search path ... OK
* loading checks for arch 'x64'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
** checking loading without being on the library search path ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking installed files from 'inst/doc' ... OK
* checking files in 'vignettes' ... OK
* checking examples ...
** running examples for arch 'i386' ... OK
Examples with CPU or elapsed time > 5s
user system elapsed
plot-methods 11.26 0.88 47.76
corFinder 8.17 0.31 12.09
doppelgangR 4.51 0.24 21.32
** running examples for arch 'x64' ... OK
Examples with CPU or elapsed time > 5s
user system elapsed
plot-methods 13.79 1.12 52.93
corFinder 7.87 0.27 8.17
doppelgangR 4.61 0.35 26.46
* checking for unstated dependencies in 'tests' ... OK
* checking tests ...
** running tests for arch 'i386' ...
Running 'maintest.R'
Warning message:
running command '"D:/biocbld/BBS-3˜1.4-B/R/bin/i386/R" CMD BATCH --vanilla "maintest.R" "maintest.Rout"' had status 1
ERROR
Running the tests in 'tests/maintest.R' failed.
Last 13 lines of output:
> ##------------------------------------------
> cat("\n")
> cat("Check caching, with a third ExpressionSet that is almost identical to the first: \n")
Check caching, with a third ExpressionSet that is almost identical to the first:
> ##------------------------------------------
> esets2 <- c(esets, esets[[1]])
> names(esets2)[3] <- "o"
> exprs(esets2[[3]]) <- exprs(esets2[[3]]) + rnorm(nrow(esets2[[3]]) * ncol(esets2[[3]]), sd=0.1)
Error in (function (od, vd) :
object and replacement value dimnames differ
Calls: exprs<- ... .validate_assayDataElementReplace -> Map -> mapply -> <Anonymous>
Execution halted
** running tests for arch 'x64' ...
Running 'maintest.R'
Warning message:
running command '"D:/biocbld/BBS-3˜1.4-B/R/bin/x64/R" CMD BATCH --vanilla "maintest.R" "maintest.Rout"' had status 1
ERROR
Running the tests in 'tests/maintest.R' failed.
Last 13 lines of output:
> ##------------------------------------------
> cat("\n")
> cat("Check caching, with a third ExpressionSet that is almost identical to the first: \n")
Check caching, with a third ExpressionSet that is almost identical to the first:
> ##------------------------------------------
> esets2 <- c(esets, esets[[1]])
> names(esets2)[3] <- "o"
> exprs(esets2[[3]]) <- exprs(esets2[[3]]) + rnorm(nrow(esets2[[3]]) * ncol(esets2[[3]]), sd=0.1)
Error in (function (od, vd) :
object and replacement value dimnames differ
Calls: exprs<- ... .validate_assayDataElementReplace -> Map -> mapply -> <Anonymous>
Execution halted
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in 'inst/doc' ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE
Status: 2 ERRORs
See
'D:/biocbld/bbs-3.4-bioc/meat/doppelgangR.Rcheck/00check.log'
for details.
maintest.Rout.fail:
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 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.
> library(RUnit)
> library(Biobase)
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit, which, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
> library(doppelgangR)
Loading required package: BiocParallel
>
> ncor <- 0; npheno <- 0; nsmoking <- 0
> options(stringsAsFactors=FALSE)
> set.seed(1)
> m1 <- matrix(rnorm(1100), ncol=11)
> colnames(m1) <- paste("m", 1:11, sep="")
> rownames(m1) <- make.names(1:nrow(m1))
> n1 <- matrix(rnorm(1000), ncol=10)
> colnames(n1) <- paste("n", 1:10, sep="")
> rownames(n1) <- make.names(1:nrow(n1))
> ##m:1 & n:1 are expression doppelgangers:
> m1[, 1] <- n1[, 1] + rnorm(100, sd=0.25); ncor <- ncor+1
> ##m:2 & m:3 are expression doppelgangers:
> m1[, 2] <- m1[, 3] + rnorm(100, sd=0.25); ncor <- ncor+1
> ##n:2 & n:3 are expression doppelgangers:
> n1[, 2] <- n1[, 3] + rnorm(100, sd=0.25); ncor <- ncor+1
> ##n:8 & n:9 are expression doppelgangers:
> n1[, 8] <- n1[, 9] + rnorm(100, sd=0.25); ncor <- ncor+1
> #n:4 & m:6 are expression doppelgangers:
> n1[, 4] <- m1[, 6] + rnorm(100, sd=0.25); ncor <- ncor+1
> #n:5 & m:4 are expression doppelgangers:
> n1[, 5] <- m1[, 4] + rnorm(100, sd=0.25); ncor <- ncor+1
>
> ##
> ##m:10 and n:10 are phenotype doppelgangers:
> m.pdata <- matrix(letters[sample(1:26, size=110, replace=TRUE)], ncol=10)
> n.pdata <- matrix(letters[sample(1:26, size=100, replace=TRUE)], ncol=10)
> n.pdata[10, ] <- m.pdata[10, ]; npheno <- npheno+1
> ##Create ExpressionSets
> m.eset <- ExpressionSet(assayData=m1)
> m.eset$id <- toupper(colnames(m1))
> pData(m.eset) <- data.frame(c(pData(m.eset), data.frame(m.pdata)))
> ##
> n.eset <- ExpressionSet(assayData=n1)
> n.eset$id <- toupper(colnames(n1))
> ##m5 and n4 are "smoking gun" doppelgangers:
> n.eset$id[4] <- "gotcha"
> m.eset$id[5] <- "gotcha"
> pData(n.eset) <- data.frame(c(pData(n.eset), data.frame(n.pdata)))
> nsmoking <- nsmoking+1
> ##
> esets <- list(m=m.eset, n=n.eset)
>
> ##------------------------------------------
> ##Check of all three types of doppelgangers:
> ##------------------------------------------
> res1 <- doppelgangR(esets, manual.smokingguns="id", automatic.smokingguns=FALSE, cache.dir=NULL)
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and n
Calculating correlations...
Found 2 batches
Adjusting for 0 covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
> df1 <- summary(res1)
>
> checkIdentical(df1[df1$sample1=="m:1" & df1$sample2=="n:1", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:2" & df1$sample2=="m:3", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:6" & df1$sample2=="n:4", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="n:2" & df1$sample2=="n:3", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:6" & df1$sample2=="n:4", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:10" & df1$sample2=="n:10", "pheno.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:5" & df1$sample2=="n:4", "smokinggun.doppel"], TRUE)
[1] TRUE
> checkEquals(nrow(df1), ncor+npheno+nsmoking)
[1] TRUE
> checkEquals(sum(df1$expr.doppel), ncor)
[1] TRUE
> checkEquals(sum(df1$pheno.doppel), npheno)
[1] TRUE
> checkEquals(sum(df1$smokinggun.doppel), nsmoking)
[1] TRUE
> checkEquals(sum(is.na(df1$expr.similarity)), 0)
[1] TRUE
> checkEquals(sum(is.na(df1$pheno.similarity)), 0)
[1] TRUE
> checkEquals(sum(is.na(df1$smokinggun.similarity)), 0)
[1] TRUE
> checkEquals(df1$id, c("M2:M3", "N2:N3", "N8:N9", "M1:N1", "gotcha:gotcha", "M6:gotcha", "M4:N5", "M10:N10"))
[1] TRUE
> for (i in match(paste("X", 1:10, sep=""), colnames(df1))){
+ cat(paste("Checking column", i, "\n"))
+ checkEquals(all(grepl("[a-z]:[a-z]", df1[[i]])), TRUE)
+ }
Checking column 10
Checking column 11
Checking column 12
Checking column 13
Checking column 14
Checking column 15
Checking column 16
Checking column 17
Checking column 18
Checking column 19
>
> ##------------------------------------------
> cat("\n")
> cat("Check without smoking guns: \n")
Check without smoking guns:
> ##------------------------------------------
> res2 <- doppelgangR(esets, smokingGunFinder.args=NULL, cache.dir=NULL)
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and n
Calculating correlations...
Found 2 batches
Adjusting for 0 covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
> df2 <- summary(res2)
> for (i in grep("pheno.similarity|smokinggun.similarity", colnames(df1), invert=TRUE)){
+ cat(paste("Checking column", i, "\n"))
+ checkEquals(df2[, i], df1[!df1$smokinggun.doppel, i])
+ }
Checking column 1
Checking column 2
Checking column 3
Checking column 4
Checking column 6
Checking column 8
Checking column 9
Checking column 10
Checking column 11
Checking column 12
Checking column 13
Checking column 14
Checking column 15
Checking column 16
Checking column 17
Checking column 18
Checking column 19
>
>
> ##------------------------------------------
> cat("\n")
> cat("Check without phenotype: \n")
Check without phenotype:
> ##------------------------------------------
> res3 <- doppelgangR(esets, phenoFinder.args=NULL, manual.smokingguns="id", automatic.smokingguns=FALSE, cache.dir=NULL)
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Working on datasets m and n
Calculating correlations...
Found 2 batches
Adjusting for 0 covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Finalizing...
> df3 <- summary(res3)
> for (i in grep("pheno.similarity", colnames(df1), invert=TRUE)){
+ cat(paste("Checking column", i, "\n"))
+ checkEquals(df3[, i], df1[!df1$pheno.doppel, i])
+ }
Checking column 1
Checking column 2
Checking column 3
Checking column 4
Checking column 6
Checking column 7
Checking column 8
Checking column 9
Checking column 10
Checking column 11
Checking column 12
Checking column 13
Checking column 14
Checking column 15
Checking column 16
Checking column 17
Checking column 18
Checking column 19
>
> ##------------------------------------------
> cat("\n")
> cat("Check without expression: \n")
Check without expression:
> ##------------------------------------------
> res4 <- doppelgangR(esets, corFinder.args=NULL, manual.smokingguns="id", automatic.smokingguns=FALSE, cache.dir=NULL)
Working on datasets m and m
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets n and n
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and n
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
> df4 <- summary(res4)
> for (i in grep("expr.similarity", colnames(df1), invert=TRUE)){
+ cat(paste("Checking column", i, "\n"))
+ checkEquals(df4[, i], df1[!df1$expr.doppel, i])
+ }
Checking column 1
Checking column 2
Checking column 4
Checking column 5
Checking column 6
Checking column 7
Checking column 8
Checking column 9
Checking column 10
Checking column 11
Checking column 12
Checking column 13
Checking column 14
Checking column 15
Checking column 16
Checking column 17
Checking column 18
Checking column 19
>
> ##------------------------------------------
> cat("\n")
> cat("Check smoking guns only: \n")
Check smoking guns only:
> ##------------------------------------------
> res4b <- doppelgangR(esets, corFinder.args=NULL, phenoFinder.args=NULL, manual.smokingguns="id", automatic.smokingguns=FALSE, cache.dir=NULL)
Working on datasets m and m
Identifying smoking-gun doppelgangers...
Working on datasets n and n
Identifying smoking-gun doppelgangers...
Working on datasets m and n
Identifying smoking-gun doppelgangers...
Finalizing...
> df4b <- summary(res4b); rownames(df4b) <- NULL
> df4b.compare <- df1[df1$smokinggun.doppel, ]; rownames(df4b.compare) <- NULL
> checkIdentical(df4b.compare[, -3:-6], df4b[, -3:-6]) ##don't check expr and pheno columns
[1] TRUE
>
>
> ##------------------------------------------
> cat("\n")
> cat("Check pruning: \n")
Check pruning:
> ##------------------------------------------
> res5 <- doppelgangR(esets, manual.smokingguns="id", automatic.smokingguns=FALSE, intermediate.pruning=TRUE, cache.dir=NULL)
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and n
Calculating correlations...
Found 2 batches
Adjusting for 0 covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
> df5 <- summary(res5)
> checkEquals(df1, df5)
[1] TRUE
>
> ##------------------------------------------
> cat("\n")
> cat("Check caching, with a third ExpressionSet that is almost identical to the first: \n")
Check caching, with a third ExpressionSet that is almost identical to the first:
> ##------------------------------------------
> esets2 <- c(esets, esets[[1]])
> names(esets2)[3] <- "o"
> exprs(esets2[[3]]) <- exprs(esets2[[3]]) + rnorm(nrow(esets2[[3]]) * ncol(esets2[[3]]), sd=0.1)
Error in (function (od, vd) :
object and replacement value dimnames differ
Calls: exprs<- ... .validate_assayDataElementReplace -> Map -> mapply -> <Anonymous>
Execution halted
maintest.Rout.fail:
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 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.
> library(RUnit)
> library(Biobase)
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit, which, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
> library(doppelgangR)
Loading required package: BiocParallel
>
> ncor <- 0; npheno <- 0; nsmoking <- 0
> options(stringsAsFactors=FALSE)
> set.seed(1)
> m1 <- matrix(rnorm(1100), ncol=11)
> colnames(m1) <- paste("m", 1:11, sep="")
> rownames(m1) <- make.names(1:nrow(m1))
> n1 <- matrix(rnorm(1000), ncol=10)
> colnames(n1) <- paste("n", 1:10, sep="")
> rownames(n1) <- make.names(1:nrow(n1))
> ##m:1 & n:1 are expression doppelgangers:
> m1[, 1] <- n1[, 1] + rnorm(100, sd=0.25); ncor <- ncor+1
> ##m:2 & m:3 are expression doppelgangers:
> m1[, 2] <- m1[, 3] + rnorm(100, sd=0.25); ncor <- ncor+1
> ##n:2 & n:3 are expression doppelgangers:
> n1[, 2] <- n1[, 3] + rnorm(100, sd=0.25); ncor <- ncor+1
> ##n:8 & n:9 are expression doppelgangers:
> n1[, 8] <- n1[, 9] + rnorm(100, sd=0.25); ncor <- ncor+1
> #n:4 & m:6 are expression doppelgangers:
> n1[, 4] <- m1[, 6] + rnorm(100, sd=0.25); ncor <- ncor+1
> #n:5 & m:4 are expression doppelgangers:
> n1[, 5] <- m1[, 4] + rnorm(100, sd=0.25); ncor <- ncor+1
>
> ##
> ##m:10 and n:10 are phenotype doppelgangers:
> m.pdata <- matrix(letters[sample(1:26, size=110, replace=TRUE)], ncol=10)
> n.pdata <- matrix(letters[sample(1:26, size=100, replace=TRUE)], ncol=10)
> n.pdata[10, ] <- m.pdata[10, ]; npheno <- npheno+1
> ##Create ExpressionSets
> m.eset <- ExpressionSet(assayData=m1)
> m.eset$id <- toupper(colnames(m1))
> pData(m.eset) <- data.frame(c(pData(m.eset), data.frame(m.pdata)))
> ##
> n.eset <- ExpressionSet(assayData=n1)
> n.eset$id <- toupper(colnames(n1))
> ##m5 and n4 are "smoking gun" doppelgangers:
> n.eset$id[4] <- "gotcha"
> m.eset$id[5] <- "gotcha"
> pData(n.eset) <- data.frame(c(pData(n.eset), data.frame(n.pdata)))
> nsmoking <- nsmoking+1
> ##
> esets <- list(m=m.eset, n=n.eset)
>
> ##------------------------------------------
> ##Check of all three types of doppelgangers:
> ##------------------------------------------
> res1 <- doppelgangR(esets, manual.smokingguns="id", automatic.smokingguns=FALSE, cache.dir=NULL)
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and n
Calculating correlations...
Found 2 batches
Adjusting for 0 covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
> df1 <- summary(res1)
>
> checkIdentical(df1[df1$sample1=="m:1" & df1$sample2=="n:1", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:2" & df1$sample2=="m:3", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:6" & df1$sample2=="n:4", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="n:2" & df1$sample2=="n:3", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:6" & df1$sample2=="n:4", "expr.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:10" & df1$sample2=="n:10", "pheno.doppel"], TRUE)
[1] TRUE
> checkIdentical(df1[df1$sample1=="m:5" & df1$sample2=="n:4", "smokinggun.doppel"], TRUE)
[1] TRUE
> checkEquals(nrow(df1), ncor+npheno+nsmoking)
[1] TRUE
> checkEquals(sum(df1$expr.doppel), ncor)
[1] TRUE
> checkEquals(sum(df1$pheno.doppel), npheno)
[1] TRUE
> checkEquals(sum(df1$smokinggun.doppel), nsmoking)
[1] TRUE
> checkEquals(sum(is.na(df1$expr.similarity)), 0)
[1] TRUE
> checkEquals(sum(is.na(df1$pheno.similarity)), 0)
[1] TRUE
> checkEquals(sum(is.na(df1$smokinggun.similarity)), 0)
[1] TRUE
> checkEquals(df1$id, c("M2:M3", "N2:N3", "N8:N9", "M1:N1", "gotcha:gotcha", "M6:gotcha", "M4:N5", "M10:N10"))
[1] TRUE
> for (i in match(paste("X", 1:10, sep=""), colnames(df1))){
+ cat(paste("Checking column", i, "\n"))
+ checkEquals(all(grepl("[a-z]:[a-z]", df1[[i]])), TRUE)
+ }
Checking column 10
Checking column 11
Checking column 12
Checking column 13
Checking column 14
Checking column 15
Checking column 16
Checking column 17
Checking column 18
Checking column 19
>
> ##------------------------------------------
> cat("\n")
> cat("Check without smoking guns: \n")
Check without smoking guns:
> ##------------------------------------------
> res2 <- doppelgangR(esets, smokingGunFinder.args=NULL, cache.dir=NULL)
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and n
Calculating correlations...
Found 2 batches
Adjusting for 0 covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data
Identifying correlation doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
> df2 <- summary(res2)
> for (i in grep("pheno.similarity|smokinggun.similarity", colnames(df1), invert=TRUE)){
+ cat(paste("Checking column", i, "\n"))
+ checkEquals(df2[, i], df1[!df1$smokinggun.doppel, i])
+ }
Checking column 1
Checking column 2
Checking column 3
Checking column 4
Checking column 6
Checking column 8
Checking column 9
Checking column 10
Checking column 11
Checking column 12
Checking column 13
Checking column 14
Checking column 15
Checking column 16
Checking column 17
Checking column 18
Checking column 19
>
>
> ##------------------------------------------
> cat("\n")
> cat("Check without phenotype: \n")
Check without phenotype:
> ##------------------------------------------
> res3 <- doppelgangR(esets, phenoFinder.args=NULL, manual.smokingguns="id", automatic.smokingguns=FALSE, cache.dir=NULL)
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Working on datasets m and n
Calculating correlations...
Found 2 batches
Adjusting for 0 covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Finalizing...
> df3 <- summary(res3)
> for (i in grep("pheno.similarity", colnames(df1), invert=TRUE)){
+ cat(paste("Checking column", i, "\n"))
+ checkEquals(df3[, i], df1[!df1$pheno.doppel, i])
+ }
Checking column 1
Checking column 2
Checking column 3
Checking column 4
Checking column 6
Checking column 7
Checking column 8
Checking column 9
Checking column 10
Checking column 11
Checking column 12
Checking column 13
Checking column 14
Checking column 15
Checking column 16
Checking column 17
Checking column 18
Checking column 19
>
> ##------------------------------------------
> cat("\n")
> cat("Check without expression: \n")
Check without expression:
> ##------------------------------------------
> res4 <- doppelgangR(esets, corFinder.args=NULL, manual.smokingguns="id", automatic.smokingguns=FALSE, cache.dir=NULL)
Working on datasets m and m
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets n and n
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and n
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
> df4 <- summary(res4)
> for (i in grep("expr.similarity", colnames(df1), invert=TRUE)){
+ cat(paste("Checking column", i, "\n"))
+ checkEquals(df4[, i], df1[!df1$expr.doppel, i])
+ }
Checking column 1
Checking column 2
Checking column 4
Checking column 5
Checking column 6
Checking column 7
Checking column 8
Checking column 9
Checking column 10
Checking column 11
Checking column 12
Checking column 13
Checking column 14
Checking column 15
Checking column 16
Checking column 17
Checking column 18
Checking column 19
>
> ##------------------------------------------
> cat("\n")
> cat("Check smoking guns only: \n")
Check smoking guns only:
> ##------------------------------------------
> res4b <- doppelgangR(esets, corFinder.args=NULL, phenoFinder.args=NULL, manual.smokingguns="id", automatic.smokingguns=FALSE, cache.dir=NULL)
Working on datasets m and m
Identifying smoking-gun doppelgangers...
Working on datasets n and n
Identifying smoking-gun doppelgangers...
Working on datasets m and n
Identifying smoking-gun doppelgangers...
Finalizing...
> df4b <- summary(res4b); rownames(df4b) <- NULL
> df4b.compare <- df1[df1$smokinggun.doppel, ]; rownames(df4b.compare) <- NULL
> checkIdentical(df4b.compare[, -3:-6], df4b[, -3:-6]) ##don't check expr and pheno columns
[1] TRUE
>
>
> ##------------------------------------------
> cat("\n")
> cat("Check pruning: \n")
Check pruning:
> ##------------------------------------------
> res5 <- doppelgangR(esets, manual.smokingguns="id", automatic.smokingguns=FALSE, intermediate.pruning=TRUE, cache.dir=NULL)
Working on datasets m and m
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets n and n
Calculating correlations...
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Working on datasets m and n
Calculating correlations...
Found 2 batches
Adjusting for 0 covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors
Finding parametric adjustments
Adjusting the Data
Identifying correlation doppelgangers...
Identifying smoking-gun doppelgangers...
Calculating phenotype similarities...
Identifying phenotype doppelgangers...
Finalizing...
> df5 <- summary(res5)
> checkEquals(df1, df5)
[1] TRUE
>
> ##------------------------------------------
> cat("\n")
> cat("Check caching, with a third ExpressionSet that is almost identical to the first: \n")
Check caching, with a third ExpressionSet that is almost identical to the first:
> ##------------------------------------------
> esets2 <- c(esets, esets[[1]])
> names(esets2)[3] <- "o"
> exprs(esets2[[3]]) <- exprs(esets2[[3]]) + rnorm(nrow(esets2[[3]]) * ncol(esets2[[3]]), sd=0.1)
Error in (function (od, vd) :
object and replacement value dimnames differ
Calls: exprs<- ... .validate_assayDataElementReplace -> Map -> mapply -> <Anonymous>
Execution halted
doppelgangR.Rcheck/00install.out:
install for i386 * installing *source* package 'doppelgangR' ... ** R ** inst ** preparing package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded install for x64 * installing *source* package 'doppelgangR' ... ** testing if installed package can be loaded * MD5 sums packaged installation of 'doppelgangR' as doppelgangR_1.1.2.zip * DONE (doppelgangR)
doppelgangR.Rcheck/examples_i386/doppelgangR-Ex.timings:
| name | user | system | elapsed | |
| corFinder | 8.17 | 0.31 | 12.09 | |
| doppelgangR | 4.51 | 0.24 | 21.32 | |
| dst | 0 | 0 | 0 | |
| mst.mle | 0.27 | 0.00 | 0.26 | |
| outlierFinder | 2.37 | 0.06 | 2.44 | |
| phenoDist | 3.70 | 0.09 | 3.79 | |
| phenoFinder | 3.76 | 0.11 | 3.87 | |
| plot-methods | 11.26 | 0.88 | 47.76 | |
| smokingGunFinder | 4.21 | 0.14 | 4.36 | |
| vectorHammingDist | 0 | 0 | 0 | |
| vectorWeightedDist | 0 | 0 | 0 | |
doppelgangR.Rcheck/examples_x64/doppelgangR-Ex.timings:
| name | user | system | elapsed | |
| corFinder | 7.87 | 0.27 | 8.17 | |
| doppelgangR | 4.61 | 0.35 | 26.46 | |
| dst | 0 | 0 | 0 | |
| mst.mle | 0.16 | 0.00 | 0.15 | |
| outlierFinder | 2.36 | 0.09 | 2.45 | |
| phenoDist | 4.77 | 0.19 | 4.96 | |
| phenoFinder | 4.35 | 0.19 | 4.56 | |
| plot-methods | 13.79 | 1.12 | 52.93 | |
| smokingGunFinder | 3.32 | 0.16 | 3.48 | |
| vectorHammingDist | 0 | 0 | 0 | |
| vectorWeightedDist | 0 | 0 | 0 | |