Pablo Flores
| Snapshot Date: 2025-03-19 13:40 -0400 (Wed, 19 Mar 2025) |
| git_url: https://git.bioconductor.org/packages/goSorensen |
| git_branch: devel |
| git_last_commit: a5e228c |
| git_last_commit_date: 2025-03-18 19:26:14 -0400 (Tue, 18 Mar 2025) |
| Back to Multiple platform build/check report for BioC 3.21: simplified long |
|
This page was generated on 2025-03-20 11:41 -0400 (Thu, 20 Mar 2025).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo1 | Linux (Ubuntu 24.04.1 LTS) | x86_64 | R Under development (unstable) (2025-03-13 r87965) -- "Unsuffered Consequences" | 4777 |
| palomino7 | Windows Server 2022 Datacenter | x64 | R Under development (unstable) (2025-03-01 r87860 ucrt) -- "Unsuffered Consequences" | 4545 |
| lconway | macOS 12.7.1 Monterey | x86_64 | R Under development (unstable) (2025-03-02 r87868) -- "Unsuffered Consequences" | 4576 |
| kjohnson3 | macOS 13.7.1 Ventura | arm64 | R Under development (unstable) (2025-03-02 r87868) -- "Unsuffered Consequences" | 4528 |
| kunpeng2 | Linux (openEuler 24.03 LTS) | aarch64 | R Under development (unstable) (2025-02-19 r87757) -- "Unsuffered Consequences" | 4458 |
| Click on any hostname to see more info about the system (e.g. compilers) (*) as reported by 'uname -p', except on Windows and Mac OS X | ||||
| Package 896/2313 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| goSorensen 1.9.0 (landing page) Pablo Flores
| nebbiolo1 | Linux (Ubuntu 24.04.1 LTS) / x86_64 | OK | OK | OK | | ||||||||
| palomino7 | Windows Server 2022 Datacenter / x64 | OK | OK | OK | OK | | ||||||||
| lconway | macOS 12.7.1 Monterey / x86_64 | OK | OK | OK | OK | | ||||||||
| kjohnson3 | macOS 13.7.1 Ventura / arm64 | OK | OK | OK | OK | | ||||||||
| kunpeng2 | Linux (openEuler 24.03 LTS) / aarch64 | OK | OK | OK | ||||||||||
|
To the developers/maintainers of the goSorensen package: - Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/goSorensen.git to reflect on this report. See Troubleshooting Build Report for more information. - Use the following Renviron settings to reproduce errors and warnings. - If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information. |
| Package: goSorensen |
| Version: 1.9.0 |
| Command: E:\biocbuild\bbs-3.21-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:goSorensen.install-out.txt --library=E:\biocbuild\bbs-3.21-bioc\R\library --no-vignettes --timings goSorensen_1.9.0.tar.gz |
| StartedAt: 2025-03-20 01:58:35 -0400 (Thu, 20 Mar 2025) |
| EndedAt: 2025-03-20 02:07:35 -0400 (Thu, 20 Mar 2025) |
| EllapsedTime: 539.9 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: goSorensen.Rcheck |
| Warnings: 0 |
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###
### Running command:
###
### E:\biocbuild\bbs-3.21-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:goSorensen.install-out.txt --library=E:\biocbuild\bbs-3.21-bioc\R\library --no-vignettes --timings goSorensen_1.9.0.tar.gz
###
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* using log directory 'E:/biocbuild/bbs-3.21-bioc/meat/goSorensen.Rcheck'
* using R Under development (unstable) (2025-03-01 r87860 ucrt)
* using platform: x86_64-w64-mingw32
* R was compiled by
gcc.exe (GCC) 13.3.0
GNU Fortran (GCC) 13.3.0
* running under: Windows Server 2022 x64 (build 20348)
* using session charset: UTF-8
* using option '--no-vignettes'
* checking for file 'goSorensen/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'goSorensen' version '1.9.0'
* package encoding: UTF-8
* 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 'goSorensen' 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 code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* 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 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 contents of 'data' directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking files in 'vignettes' ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
buildEnrichTable 72.39 4.46 76.91
enrichedIn 63.71 4.96 68.72
* checking for unstated dependencies in 'tests' ... OK
* checking tests ...
Running 'test_gosorensen_funcs.R'
OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE
Status: OK
goSorensen.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### E:\biocbuild\bbs-3.21-bioc\R\bin\R.exe CMD INSTALL goSorensen ### ############################################################################## ############################################################################## * installing to library 'E:/biocbuild/bbs-3.21-bioc/R/library' * installing *source* package 'goSorensen' ... ** this is package 'goSorensen' version '1.9.0' ** using staged installation Warning in person1(given = given[[i]], family = family[[i]], middle = middle[[i]], : Invalid ORCID iD: '0000-0002-4736-699'. ** R ** data ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (goSorensen)
goSorensen.Rcheck/tests/test_gosorensen_funcs.Rout
R Under development (unstable) (2025-03-01 r87860 ucrt) -- "Unsuffered Consequences"
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> library(goSorensen)
Attaching package: 'goSorensen'
The following object is masked from 'package:utils':
upgrade
>
> # A contingency table of GO terms mutual enrichment
> # between gene lists "atlas" and "sanger":
> data("cont_atlas.sanger_BP4")
> cont_atlas.sanger_BP4
Enriched in sanger
Enriched in atlas TRUE FALSE
TRUE 201 212
FALSE 29 3465
> ?cont_atlas.sanger_BP4
> class(cont_atlas.sanger_BP4)
[1] "table"
>
> # Sorensen-Dice dissimilarity on this contingency table:
> ?dSorensen
> dSorensen(cont_atlas.sanger_BP4)
[1] 0.3748056
>
> # Standard error of this Sorensen-Dice dissimilarity estimate:
> ?seSorensen
> seSorensen(cont_atlas.sanger_BP4)
[1] 0.02240875
>
> # Upper 95% confidence limit for the Sorensen-Dice dissimilarity:
> ?duppSorensen
> duppSorensen(cont_atlas.sanger_BP4)
[1] 0.4116647
> # This confidence limit is based on an assimptotic normal N(0,1)
> # approximation to the distribution of (dSampl - d) / se, where
> # dSampl stands for the sample dissimilarity, d for the true dissimilarity
> # and se for the sample dissimilarity standard error estimate.
>
> # Upper confidence limit but using a Student's t instead of a N(0,1)
> # (just as an example, not recommended -no theoretical justification)
> df <- sum(cont_atlas.sanger_BP4[1:3]) - 2
> duppSorensen(cont_atlas.sanger_BP4, z.conf.level = qt(1 - 0.95, df))
[1] 0.4117425
>
> # Upper confidence limit but using a bootstrap approximation
> # to the sampling distribution, instead of a N(0,1)
> set.seed(123)
> duppSorensen(cont_atlas.sanger_BP4, boot = TRUE)
[1] 0.4124639
attr(,"eff.nboot")
[1] 10000
>
> # Some computations on diverse data structures:
> badConti <- as.table(matrix(c(501, 27, 36, 12, 43, 15, 0, 0, 0),
+ nrow = 3, ncol = 3,
+ dimnames = list(c("a1","a2","a3"),
+ c("b1", "b2","b3"))))
> tryCatch(nice2x2Table(badConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(badConti): Not a 2x2 table>
>
> incompleteConti <- badConti[1,1:min(2,ncol(badConti)), drop = FALSE]
> incompleteConti
b1 b2
a1 501 12
> tryCatch(nice2x2Table(incompleteConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(incompleteConti): Not a 2x2 table>
>
> contiAsVector <- c(32, 21, 81, 1439)
> nice2x2Table(contiAsVector)
[1] TRUE
> contiAsVector.mat <- matrix(contiAsVector, nrow = 2)
> contiAsVector.mat
[,1] [,2]
[1,] 32 81
[2,] 21 1439
> contiAsVectorLen3 <- c(32, 21, 81)
> nice2x2Table(contiAsVectorLen3)
[1] TRUE
>
> tryCatch(dSorensen(badConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(x): Not a 2x2 table>
>
> # Apparently, the next order works fine, but returns a wrong value!
> dSorensen(badConti, check.table = FALSE)
[1] 0.05915493
>
> tryCatch(dSorensen(incompleteConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(x): Not a 2x2 table>
> dSorensen(contiAsVector)
[1] 0.6144578
> dSorensen(contiAsVector.mat)
[1] 0.6144578
> dSorensen(contiAsVectorLen3)
[1] 0.6144578
> dSorensen(contiAsVectorLen3, check.table = FALSE)
[1] 0.6144578
> dSorensen(c(0,0,0,45))
[1] NaN
>
> tryCatch(seSorensen(badConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(x): Not a 2x2 table>
> tryCatch(seSorensen(incompleteConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(x): Not a 2x2 table>
> seSorensen(contiAsVector)
[1] 0.04818012
> seSorensen(contiAsVector.mat)
[1] 0.04818012
> seSorensen(contiAsVectorLen3)
[1] 0.04818012
> seSorensen(contiAsVectorLen3, check.table = FALSE)
[1] 0.04818012
> tryCatch(seSorensen(contiAsVectorLen3, check.table = "not"), error = function(e) {return(e)})
<simpleError in seSorensen.numeric(contiAsVectorLen3, check.table = "not"): Argument 'check.table' must be logical>
> seSorensen(c(0,0,0,45))
[1] NaN
>
> tryCatch(duppSorensen(badConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(x): Not a 2x2 table>
> tryCatch(duppSorensen(incompleteConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(x): Not a 2x2 table>
> duppSorensen(contiAsVector)
[1] 0.6937071
> duppSorensen(contiAsVector.mat)
[1] 0.6937071
> set.seed(123)
> duppSorensen(contiAsVector, boot = TRUE)
[1] 0.6922658
attr(,"eff.nboot")
[1] 10000
> set.seed(123)
> duppSorensen(contiAsVector.mat, boot = TRUE)
[1] 0.6922658
attr(,"eff.nboot")
[1] 10000
> duppSorensen(contiAsVectorLen3)
[1] 0.6937071
> # Bootstrapping requires full contingency tables (4 values)
> set.seed(123)
> tryCatch(duppSorensen(contiAsVectorLen3, boot = TRUE), error = function(e) {return(e)})
<simpleError in duppSorensen.numeric(contiAsVectorLen3, boot = TRUE): Bootstraping requires a numeric vector of 4 frequencies>
> duppSorensen(c(0,0,0,45))
[1] NaN
>
> # Equivalence test, H0: d >= d0 vs H1: d < d0 (d0 = 0.4444)
> ?equivTestSorensen
> equiv.atlas.sanger <- equivTestSorensen(cont_atlas.sanger_BP4)
> equiv.atlas.sanger
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: cont_atlas.sanger_BP4
(d - d0) / se = -3.1077, p-value = 0.0009429
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.4116647
sample estimates:
Sorensen dissimilarity
0.3748056
attr(,"se")
standard error
0.02240875
> getTable(equiv.atlas.sanger)
Enriched in sanger
Enriched in atlas TRUE FALSE
TRUE 201 212
FALSE 29 3465
> getPvalue(equiv.atlas.sanger)
p-value
0.0009428632
>
> tryCatch(equivTestSorensen(badConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(x): Not a 2x2 table>
> tryCatch(equivTestSorensen(incompleteConti), error = function(e) {return(e)})
<simpleError in nice2x2Table.table(x): Not a 2x2 table>
> equivTestSorensen(contiAsVector)
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: contiAsVector
(d - d0) / se = 3.5287, p-value = 0.9998
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6937071
sample estimates:
Sorensen dissimilarity
0.6144578
attr(,"se")
standard error
0.04818012
> equivTestSorensen(contiAsVector.mat)
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: contiAsVector.mat
(d - d0) / se = 3.5287, p-value = 0.9998
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6937071
sample estimates:
Sorensen dissimilarity
0.6144578
attr(,"se")
standard error
0.04818012
> set.seed(123)
> equivTestSorensen(contiAsVector.mat, boot = TRUE)
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: contiAsVector.mat
(d - d0) / se = 3.5287, p-value = 0.9996
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6922658
sample estimates:
Sorensen dissimilarity
0.6144578
attr(,"se")
standard error
0.04818012
> equivTestSorensen(contiAsVectorLen3)
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: contiAsVectorLen3
(d - d0) / se = 3.5287, p-value = 0.9998
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6937071
sample estimates:
Sorensen dissimilarity
0.6144578
attr(,"se")
standard error
0.04818012
>
> tryCatch(equivTestSorensen(contiAsVectorLen3, boot = TRUE), error = function(e) {return(e)})
<simpleError in equivTestSorensen.numeric(contiAsVectorLen3, boot = TRUE): Bootstraping requires a numeric vector of 4 frequencies>
>
> equivTestSorensen(c(0,0,0,45))
No test performed due non finite (d - d0) / se statistic
data: c(0, 0, 0, 45)
(d - d0) / se = NaN, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
NaN
attr(,"se")
standard error
NaN
>
> # Sorensen-Dice computations from scratch, directly from gene lists
> data(allOncoGeneLists)
> ?allOncoGeneLists
>
> library(org.Hs.eg.db)
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: generics
Attaching package: 'generics'
The following objects are masked from 'package:base':
as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
setequal, union
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
as.data.frame, basename, cbind, colnames, dirname, do.call,
duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
unsplit, which.max, which.min
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: IRanges
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following object is masked from 'package:utils':
findMatches
The following objects are masked from 'package:base':
I, expand.grid, unname
Attaching package: 'IRanges'
The following object is masked from 'package:grDevices':
windows
> humanEntrezIDs <- keys(org.Hs.eg.db, keytype = "ENTREZID")
> # First, the mutual GO node enrichment tables are built, then computations
> # proceed from these contingency tables.
> # Building the contingency tables is a slow process (many enrichment tests)
> normTest <- equivTestSorensen(allOncoGeneLists[["atlas"]], allOncoGeneLists[["sanger"]],
+ listNames = c("atlas", "sanger"),
+ onto = "BP", GOLevel = 5,
+ geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> normTest
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -8.5125, p-value < 2.2e-16
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3482836
sample estimates:
Sorensen dissimilarity
0.3252525
attr(,"se")
standard error
0.01400193
>
> # To perform a bootstrap test from scratch would be even slower:
> # set.seed(123)
> # bootTest <- equivTestSorensen(allOncoGeneLists[["atlas"]], allOncoGeneLists[["sanger"]],
> # listNames = c("atlas", "sanger"),
> # boot = TRUE,
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> # bootTest
>
> # It is much faster to upgrade 'normTest' to be a bootstrap test:
> set.seed(123)
> bootTest <- upgrade(normTest, boot = TRUE)
> bootTest
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -8.5125, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3484472
sample estimates:
Sorensen dissimilarity
0.3252525
attr(,"se")
standard error
0.01400193
> # To know the number of planned bootstrap replicates:
> getNboot(bootTest)
[1] 10000
> # To know the number of valid bootstrap replicates:
> getEffNboot(bootTest)
[1] 10000
>
> # There are similar methods for dSorensen, seSorensen, duppSorensen, etc. to
> # compute directly from a pair of gene lists.
> # They are quite slow for the same reason as before (many enrichment tests).
> # dSorensen(allOncoGeneLists[["atlas"]], allOncoGeneLists[["sanger"]],
> # listNames = c("atlas", "sanger"),
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> # seSorensen(allOncoGeneLists[["atlas"]], allOncoGeneLists[["sanger"]],
> # listNames = c("atlas", "sanger"),
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> #
> # duppSorensen(allOncoGeneLists[["atlas"]], allOncoGeneLists[["sanger"]],
> # listNames = c("atlas", "sanger"),
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> #
> # set.seed(123)
> # duppSorensen(allOncoGeneLists[["atlas"]], allOncoGeneLists[["sanger"]],
> # boot = TRUE,
> # listNames = c("atlas", "sanger"),
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> # etc.
>
> # To build the contingency table first and then compute from it, may be a more flexible
> # and saving time strategy, in general:
> ?buildEnrichTable
> tab <- buildEnrichTable(allOncoGeneLists[["atlas"]], allOncoGeneLists[["sanger"]],
+ listNames = c("atlas", "sanger"),
+ onto = "BP", GOLevel = 5,
+ geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
>
> tab
Enriched in sanger
Enriched in atlas TRUE FALSE
TRUE 501 429
FALSE 54 8085
>
> # (Here, an obvious faster possibility would be to recover the enrichment contingency
> # table from the previous normal test result:)
> tab <- getTable(normTest)
> tab
Enriched in sanger
Enriched in atlas TRUE FALSE
TRUE 501 429
FALSE 54 8085
>
> tst <- equivTestSorensen(tab)
> tst
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -8.5125, p-value < 2.2e-16
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3482836
sample estimates:
Sorensen dissimilarity
0.3252525
attr(,"se")
standard error
0.01400193
> set.seed(123)
> bootTst <- equivTestSorensen(tab, boot = TRUE)
> bootTst
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -8.5125, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3484472
sample estimates:
Sorensen dissimilarity
0.3252525
attr(,"se")
standard error
0.01400193
>
> dSorensen(tab)
[1] 0.3252525
> seSorensen(tab)
[1] 0.01400193
> # or:
> getDissimilarity(tst)
Sorensen dissimilarity
0.3252525
attr(,"se")
standard error
0.01400193
>
> duppSorensen(tab)
[1] 0.3482836
> getUpper(tst)
dUpper
0.3482836
>
> set.seed(123)
> duppSorensen(tab, boot = TRUE)
[1] 0.3484472
attr(,"eff.nboot")
[1] 10000
> getUpper(bootTst)
dUpper
0.3484472
>
> # To perform from scratch all pairwise tests (or other Sorensen-Dice computations)
> # is even much slower. For example, all pairwise...
> # Dissimilarities:
> # # allPairDiss <- dSorensen(allOncoGeneLists,
> # # onto = "BP", GOLevel = 5,
> # # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> # # allPairDiss
> #
> # # Still time consuming but potentially faster: compute in parallel (more precisely,
> # # build all enrichment tables in parallel):
> # allPairDiss <- dSorensen(allOncoGeneLists,
> # onto = "BP", GOLevel = 4,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db",
> # parallel = TRUE)
> # allPairDiss
> # # Not always parallelization results in speed-up, take into account the trade-off between
> # # parallelization initialization and possible gain in speed. For a few gene lists (like
> # # in this example, 7 lists, a negative speed-up will be the most common scenario)
>
> # Standard errors:
> # seSorensen(allOncoGeneLists,
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> #
> # Upper confidence interval limits:
> # duppSorensen(allOncoGeneLists,
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> # All pairwise asymptotic normal tests:
> # allTests <- equivTestSorensen(allOncoGeneLists,
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> # getPvalue(allTests, simplify = FALSE)
> # getPvalue(allTests)
> # p.adjust(getPvalue(allTests), method = "holm")
> # To perform all pairwise bootstrap tests from scratch is (slightly)
> # even more time consuming:
> # set.seed(123)
> # allBootTests <- equivTestSorensen(allOncoGeneLists,
> # boot = TRUE,
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> # Not all bootstrap replicates may conduct to finite statistics:
> # getNboot(allBootTests)
>
> # Given the normal tests (object 'allTests'), it is much faster to upgrade
> # it to have the bootstrap tests:
> # set.seed(123)
> # allBootTests <- upgrade(allTests, boot = TRUE)
> # getPvalue(allBootTests, simplify = FALSE)
>
> # Again, the faster and more flexible possibility may be:
> # 1) First, build all pairwise enrichment contingency tables (slow first step):
> # allTabsBP.4 <- buildEnrichTable(allOncoGeneLists,
> # onto = "BP", GOLevel = 5,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db")
> # allTabsBP.4
>
> # Better, directly use the dataset available at this package, goSorensen:
> data("cont_all_BP4")
> cont_all_BP4
$cangenes
$cangenes$atlas
Enriched in atlas
Enriched in cangenes TRUE FALSE
TRUE 0 0
FALSE 413 3494
attr(,"onto")
[1] "BP"
attr(,"GOLevel")
[1] 4
$cis
$cis$atlas
Enriched in atlas
Enriched in cis TRUE FALSE
TRUE 75 6
FALSE 338 3488
$cis$cangenes
Enriched in cangenes
Enriched in cis TRUE FALSE
TRUE 0 81
FALSE 0 3826
attr(,"onto")
[1] "BP"
attr(,"GOLevel")
[1] 4
$miscellaneous
$miscellaneous$atlas
Enriched in atlas
Enriched in miscellaneous TRUE FALSE
TRUE 191 26
FALSE 222 3468
$miscellaneous$cangenes
Enriched in cangenes
Enriched in miscellaneous TRUE FALSE
TRUE 0 217
FALSE 0 3690
attr(,"onto")
[1] "BP"
attr(,"GOLevel")
[1] 4
$miscellaneous$cis
Enriched in cis
Enriched in miscellaneous TRUE FALSE
TRUE 67 150
FALSE 14 3676
$sanger
$sanger$atlas
Enriched in atlas
Enriched in sanger TRUE FALSE
TRUE 201 29
FALSE 212 3465
$sanger$cangenes
Enriched in cangenes
Enriched in sanger TRUE FALSE
TRUE 0 230
FALSE 0 3677
attr(,"onto")
[1] "BP"
attr(,"GOLevel")
[1] 4
$sanger$cis
Enriched in cis
Enriched in sanger TRUE FALSE
TRUE 64 166
FALSE 17 3660
$sanger$miscellaneous
Enriched in miscellaneous
Enriched in sanger TRUE FALSE
TRUE 155 75
FALSE 62 3615
$Vogelstein
$Vogelstein$atlas
Enriched in atlas
Enriched in Vogelstein TRUE FALSE
TRUE 217 35
FALSE 196 3459
$Vogelstein$cangenes
Enriched in cangenes
Enriched in Vogelstein TRUE FALSE
TRUE 0 252
FALSE 0 3655
attr(,"onto")
[1] "BP"
attr(,"GOLevel")
[1] 4
$Vogelstein$cis
Enriched in cis
Enriched in Vogelstein TRUE FALSE
TRUE 63 189
FALSE 18 3637
$Vogelstein$miscellaneous
Enriched in miscellaneous
Enriched in Vogelstein TRUE FALSE
TRUE 155 97
FALSE 62 3593
$Vogelstein$sanger
Enriched in sanger
Enriched in Vogelstein TRUE FALSE
TRUE 213 39
FALSE 17 3638
$waldman
$waldman$atlas
Enriched in atlas
Enriched in waldman TRUE FALSE
TRUE 255 41
FALSE 158 3453
$waldman$cangenes
Enriched in cangenes
Enriched in waldman TRUE FALSE
TRUE 0 296
FALSE 0 3611
attr(,"onto")
[1] "BP"
attr(,"GOLevel")
[1] 4
$waldman$cis
Enriched in cis
Enriched in waldman TRUE FALSE
TRUE 72 224
FALSE 9 3602
$waldman$miscellaneous
Enriched in miscellaneous
Enriched in waldman TRUE FALSE
TRUE 198 98
FALSE 19 3592
$waldman$sanger
Enriched in sanger
Enriched in waldman TRUE FALSE
TRUE 177 119
FALSE 53 3558
$waldman$Vogelstein
Enriched in Vogelstein
Enriched in waldman TRUE FALSE
TRUE 193 103
FALSE 59 3552
attr(,"onto")
[1] "BP"
attr(,"GOLevel")
[1] 4
attr(,"class")
[1] "tableList" "list"
attr(,"enriched")
atlas cangenes cis miscellaneous sanger Vogelstein waldman
GO:0001649 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0030278 TRUE FALSE FALSE TRUE FALSE TRUE TRUE
GO:0030279 FALSE FALSE FALSE FALSE FALSE TRUE TRUE
GO:0030282 TRUE FALSE FALSE FALSE FALSE TRUE TRUE
GO:0036075 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0045778 FALSE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0048755 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0060688 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0061138 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0002263 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0030168 TRUE FALSE FALSE TRUE TRUE FALSE TRUE
GO:0042118 FALSE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0050866 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0050867 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0061900 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0072537 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0001780 TRUE FALSE FALSE TRUE FALSE FALSE FALSE
GO:0002260 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0001818 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0002367 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0002534 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0010573 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0032602 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0032609 FALSE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0032612 TRUE FALSE FALSE FALSE FALSE TRUE FALSE
GO:0032613 TRUE FALSE FALSE FALSE TRUE FALSE FALSE
GO:0032615 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0032623 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0032633 FALSE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0032635 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0071604 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0071706 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0002562 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0002566 FALSE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0016445 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0002433 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0002443 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0002697 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0002698 TRUE FALSE TRUE TRUE FALSE FALSE TRUE
GO:0002699 TRUE FALSE TRUE FALSE TRUE TRUE TRUE
GO:0043299 TRUE FALSE TRUE FALSE TRUE TRUE TRUE
GO:0002218 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0034101 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0002377 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0002700 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0002701 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0002702 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0002200 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0048534 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0002685 TRUE FALSE TRUE TRUE FALSE FALSE TRUE
GO:1903706 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0002686 FALSE FALSE FALSE TRUE FALSE FALSE FALSE
GO:0002695 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0050777 TRUE FALSE TRUE TRUE TRUE FALSE TRUE
GO:0050858 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:1903707 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0002687 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0002696 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:1903708 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0001893 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0007281 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0007530 FALSE FALSE FALSE FALSE FALSE TRUE FALSE
GO:0007548 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0009994 FALSE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0033327 FALSE FALSE FALSE FALSE FALSE TRUE FALSE
GO:0035234 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0045136 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0045137 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0046697 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0048608 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0060008 FALSE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0060009 FALSE FALSE FALSE FALSE FALSE TRUE FALSE
GO:0060512 TRUE FALSE FALSE FALSE FALSE TRUE TRUE
GO:0060525 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0060736 TRUE FALSE FALSE FALSE FALSE TRUE TRUE
GO:0060740 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0060742 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0003012 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0000768 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0001666 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0002931 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0006970 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0006979 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0009408 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0033555 FALSE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0034405 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0035902 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0035966 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0042594 TRUE FALSE FALSE FALSE TRUE FALSE FALSE
GO:0055093 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0002437 FALSE FALSE TRUE TRUE FALSE FALSE TRUE
GO:0006959 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0042092 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0031023 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0032886 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0045786 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0045787 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0051321 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0007162 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0031589 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0033627 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0045785 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0030010 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0032878 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0061245 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0061339 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0009755 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0009756 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0023019 FALSE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0038034 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0008366 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0007389 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0007566 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0009791 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0046660 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0046661 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0048736 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0003002 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0009798 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0009799 FALSE FALSE FALSE FALSE FALSE TRUE FALSE
GO:0009880 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0007611 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0032922 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0042752 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0010463 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0014009 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0033002 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0033687 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0035988 TRUE FALSE FALSE FALSE FALSE TRUE FALSE
GO:0048144 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0050673 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0051450 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0061323 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0061351 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0070661 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0072089 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0072111 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0009895 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0072526 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:1901136 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0006809 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0016051 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0032964 FALSE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0042446 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0009612 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0009649 FALSE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0032102 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0042330 TRUE FALSE TRUE TRUE FALSE FALSE TRUE
GO:0071496 TRUE FALSE TRUE FALSE FALSE TRUE TRUE
GO:0002347 TRUE FALSE TRUE FALSE TRUE FALSE FALSE
GO:0002833 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0071216 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:1990840 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0009266 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0009314 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0051602 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0070482 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0071214 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0001763 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0003151 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0003179 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0003206 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0007440 TRUE FALSE FALSE FALSE FALSE TRUE FALSE
GO:0010171 FALSE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0021575 FALSE FALSE FALSE TRUE FALSE FALSE FALSE
GO:0021587 FALSE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0031069 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0035107 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0048532 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0048853 FALSE FALSE FALSE TRUE FALSE TRUE TRUE
GO:0060323 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0060325 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0060411 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0060560 TRUE FALSE FALSE TRUE FALSE TRUE TRUE
GO:0060561 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0061383 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0071697 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0072028 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0097094 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0010713 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0045833 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0045912 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0062014 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0120163 FALSE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0032352 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0045834 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0045913 TRUE FALSE FALSE FALSE FALSE TRUE TRUE
GO:0062013 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0120162 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:1904407 TRUE FALSE FALSE FALSE FALSE TRUE FALSE
GO:0001558 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0030307 FALSE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0030308 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0048588 TRUE FALSE FALSE TRUE FALSE TRUE TRUE
GO:0006887 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0045056 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0046718 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0019083 TRUE FALSE FALSE FALSE TRUE FALSE FALSE
GO:0043923 FALSE FALSE FALSE FALSE TRUE FALSE FALSE
GO:0010712 FALSE FALSE FALSE FALSE FALSE TRUE FALSE
GO:0032350 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0034248 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0060263 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0062012 TRUE FALSE FALSE FALSE FALSE TRUE TRUE
GO:0080164 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0120161 TRUE FALSE TRUE FALSE TRUE TRUE FALSE
GO:0035019 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0097150 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:1902455 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:1902459 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:2000036 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0071695 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0007051 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0007059 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0007062 FALSE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0007098 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0008608 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0010948 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0044786 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0045023 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0051304 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0051653 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0090068 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:1903046 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0022405 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0046883 TRUE FALSE FALSE FALSE FALSE TRUE TRUE
GO:0046887 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0032970 TRUE FALSE TRUE TRUE FALSE FALSE TRUE
GO:0001759 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0031295 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0099590 FALSE FALSE FALSE FALSE TRUE FALSE FALSE
GO:0007584 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0031669 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0032107 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0051282 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:1905952 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0006403 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0040014 FALSE FALSE FALSE TRUE FALSE TRUE FALSE
GO:0046620 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0046622 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0060419 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0098868 FALSE FALSE FALSE FALSE FALSE TRUE TRUE
GO:0045926 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0045927 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0048638 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0040013 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0050920 TRUE FALSE TRUE TRUE FALSE FALSE TRUE
GO:0050922 FALSE FALSE FALSE TRUE FALSE FALSE FALSE
GO:2000146 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0050921 TRUE FALSE TRUE FALSE FALSE FALSE TRUE
GO:0001101 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0006935 TRUE FALSE TRUE TRUE FALSE FALSE TRUE
GO:0009410 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0009636 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0010038 TRUE FALSE FALSE FALSE FALSE TRUE TRUE
GO:0035094 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0046677 TRUE FALSE TRUE FALSE TRUE TRUE FALSE
GO:0046683 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:1902074 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0022404 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0042633 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0022602 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0044849 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0005976 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0043502 FALSE FALSE FALSE FALSE TRUE FALSE FALSE
GO:0050435 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0043697 TRUE FALSE FALSE FALSE FALSE TRUE FALSE
GO:0006091 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0006413 TRUE FALSE FALSE TRUE FALSE FALSE FALSE
GO:0042180 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0072593 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0090398 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0090399 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0006099 FALSE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0005996 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0051702 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0007565 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0043368 TRUE FALSE TRUE FALSE TRUE TRUE FALSE
GO:0045061 FALSE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0002274 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0002366 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0048640 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0048639 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:1905954 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:2000243 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0051051 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:1900047 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:1905953 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:2000242 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0032388 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0034764 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0045739 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0051781 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:1903532 TRUE FALSE FALSE FALSE FALSE TRUE TRUE
GO:1903829 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:1905898 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0045738 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0051283 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:1903531 FALSE FALSE TRUE FALSE FALSE FALSE TRUE
GO:0060759 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0090287 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:1900076 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0070572 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:1903036 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:1903846 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0031348 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0060761 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0090288 FALSE FALSE FALSE TRUE FALSE FALSE FALSE
GO:1903035 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0001832 FALSE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0035264 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0035265 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0042246 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0055017 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0022412 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0030728 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0042698 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0060135 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0001704 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0001756 TRUE FALSE TRUE FALSE TRUE TRUE TRUE
GO:0001825 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0002467 FALSE FALSE TRUE FALSE TRUE TRUE TRUE
GO:0003188 FALSE FALSE FALSE FALSE FALSE TRUE FALSE
GO:0003272 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0006949 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0030220 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0035148 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0048645 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0060343 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0060788 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0060900 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0001974 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0034103 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0034104 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0046849 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0001541 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0001824 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0001942 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0002088 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0003157 FALSE FALSE FALSE FALSE TRUE FALSE FALSE
GO:0003170 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0003205 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0003279 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0016358 TRUE FALSE FALSE TRUE FALSE FALSE FALSE
GO:0021510 TRUE FALSE FALSE TRUE TRUE TRUE FALSE
GO:0021516 FALSE FALSE FALSE TRUE FALSE FALSE FALSE
GO:0021517 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0021536 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0021537 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0021543 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0021549 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0021670 FALSE FALSE TRUE FALSE FALSE FALSE FALSE
GO:0021675 TRUE FALSE FALSE FALSE TRUE FALSE FALSE
GO:0021766 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0021772 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0021794 FALSE FALSE FALSE FALSE FALSE TRUE FALSE
GO:0021987 TRUE FALSE FALSE TRUE FALSE TRUE TRUE
GO:0021988 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0022037 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0030900 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0030901 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0030902 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0031018 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0031099 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0032835 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0036302 TRUE FALSE FALSE FALSE FALSE TRUE FALSE
GO:0048286 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0048839 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0048857 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0060021 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0060324 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0060430 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0060711 TRUE FALSE FALSE FALSE FALSE TRUE TRUE
GO:0060749 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0061029 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0061377 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0072006 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:1902742 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:1904888 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0001708 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0001709 TRUE FALSE TRUE TRUE TRUE TRUE FALSE
GO:0010623 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0045165 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0048469 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0001659 TRUE FALSE TRUE TRUE TRUE TRUE FALSE
GO:0001894 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0048872 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0060249 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0097009 TRUE FALSE FALSE FALSE FALSE TRUE FALSE
GO:0140962 FALSE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0033500 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:1900046 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:2000241 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0010453 TRUE FALSE TRUE FALSE FALSE FALSE FALSE
GO:0040034 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0045682 TRUE FALSE FALSE FALSE FALSE TRUE TRUE
GO:0048634 FALSE FALSE TRUE TRUE FALSE FALSE TRUE
GO:0070570 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0090183 FALSE FALSE FALSE FALSE TRUE TRUE TRUE
GO:1901861 FALSE FALSE TRUE TRUE FALSE FALSE TRUE
GO:1904748 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0031641 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0034762 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0051302 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0060353 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:1900117 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0007596 TRUE FALSE FALSE TRUE FALSE FALSE FALSE
GO:0050819 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0002523 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0030595 TRUE FALSE TRUE TRUE FALSE FALSE TRUE
GO:0071674 TRUE FALSE TRUE TRUE FALSE FALSE TRUE
GO:0097529 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0032370 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0043270 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0045807 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0051047 TRUE FALSE FALSE FALSE FALSE TRUE TRUE
GO:0051222 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0051048 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0048635 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0051961 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0061037 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0070168 FALSE FALSE FALSE FALSE FALSE TRUE FALSE
GO:1901343 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:1901862 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0045684 TRUE FALSE FALSE FALSE FALSE TRUE FALSE
GO:0045830 FALSE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0048636 FALSE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0051798 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0051962 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0090184 FALSE FALSE FALSE FALSE FALSE TRUE FALSE
GO:0110110 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:1901863 FALSE FALSE FALSE TRUE TRUE TRUE TRUE
GO:1904018 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:1904179 FALSE FALSE FALSE FALSE TRUE TRUE FALSE
GO:1905332 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0051656 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0051651 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0010632 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0042634 FALSE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0010718 TRUE FALSE FALSE TRUE FALSE TRUE TRUE
GO:0045618 TRUE FALSE FALSE FALSE FALSE TRUE FALSE
GO:0045933 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:2000833 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0010633 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0014741 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0008356 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0017145 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
GO:0051446 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0050000 TRUE FALSE FALSE FALSE TRUE FALSE FALSE
GO:0051647 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:1990849 TRUE FALSE FALSE FALSE TRUE TRUE TRUE
GO:0051208 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0009615 TRUE FALSE FALSE TRUE FALSE FALSE FALSE
GO:0009620 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0104004 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0034219 FALSE FALSE FALSE TRUE TRUE TRUE FALSE
GO:0051642 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0007204 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0008360 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0010522 TRUE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0031647 TRUE FALSE FALSE TRUE TRUE TRUE FALSE
GO:0043114 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0050803 TRUE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0050878 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0090559 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0099072 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0099149 FALSE FALSE FALSE TRUE TRUE FALSE FALSE
GO:0010469 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0051090 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0051098 TRUE FALSE FALSE TRUE TRUE TRUE TRUE
GO:0019362 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0006206 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0090132 TRUE FALSE TRUE TRUE FALSE FALSE TRUE
GO:0006921 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:1900119 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0019827 TRUE FALSE TRUE TRUE TRUE TRUE TRUE
GO:0001502 FALSE FALSE FALSE TRUE FALSE FALSE TRUE
GO:0140353 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0030193 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0030195 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0042359 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
GO:0000212 FALSE FALSE FALSE TRUE TRUE FALSE FALSE
GO:0044771 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0045132 FALSE FALSE FALSE TRUE TRUE FALSE FALSE
GO:0061982 TRUE FALSE FALSE TRUE TRUE FALSE TRUE
GO:0140013 TRUE FALSE FALSE TRUE TRUE FALSE TRUE
GO:0106106 TRUE FALSE TRUE FALSE TRUE TRUE FALSE
GO:1901993 FALSE FALSE FALSE FALSE FALSE FALSE TRUE
GO:0046209 TRUE FALSE FALSE FALSE FALSE FALSE FALSE
attr(,"enriched")attr(,"nTerms")
[1] 3907
> class(cont_all_BP4)
[1] "tableList" "list"
> # 2) Then perform all required computatios from these enrichment contingency tables...
> # All pairwise tests:
> allTests <- equivTestSorensen(cont_all_BP4)
> allTests
$cangenes
$cangenes$atlas
No test performed due not finite (d - d0) / se statistic
data: tab
(d - d0) / se = Inf, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
1
attr(,"se")
standard error
0
$cis
$cis$atlas
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = 9.3376, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.7407313
sample estimates:
Sorensen dissimilarity
0.6963563
attr(,"se")
standard error
0.02697813
$cis$cangenes
No test performed due not finite (d - d0) / se statistic
data: tab
(d - d0) / se = Inf, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
1
attr(,"se")
standard error
0
$miscellaneous
$miscellaneous$atlas
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -2.208, p-value = 0.01362
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.4314904
sample estimates:
Sorensen dissimilarity
0.3936508
attr(,"se")
standard error
0.02300482
$miscellaneous$cangenes
No test performed due not finite (d - d0) / se statistic
data: tab
(d - d0) / se = Inf, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
1
attr(,"se")
standard error
0
$miscellaneous$cis
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = 2.9448, p-value = 0.9984
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6094825
sample estimates:
Sorensen dissimilarity
0.5503356
attr(,"se")
standard error
0.03595877
$sanger
$sanger$atlas
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -3.1077, p-value = 0.0009429
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.4116647
sample estimates:
Sorensen dissimilarity
0.3748056
attr(,"se")
standard error
0.02240875
$sanger$cangenes
No test performed due not finite (d - d0) / se statistic
data: tab
(d - d0) / se = Inf, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
1
attr(,"se")
standard error
0
$sanger$cis
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = 4.0855, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6463915
sample estimates:
Sorensen dissimilarity
0.5884244
attr(,"se")
standard error
0.03524148
$sanger$miscellaneous
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -5.5254, p-value = 1.643e-08
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3475558
sample estimates:
Sorensen dissimilarity
0.3064877
attr(,"se")
standard error
0.02496764
$Vogelstein
$Vogelstein$atlas
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -4.5244, p-value = 3.028e-06
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3826602
sample estimates:
Sorensen dissimilarity
0.3473684
attr(,"se")
standard error
0.0214559
$Vogelstein$cangenes
No test performed due not finite (d - d0) / se statistic
data: tab
(d - d0) / se = Inf, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
1
attr(,"se")
standard error
0
$Vogelstein$cis
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = 5.2254, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6773931
sample estimates:
Sorensen dissimilarity
0.6216216
attr(,"se")
standard error
0.03390663
$Vogelstein$miscellaneous
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -4.1614, p-value = 1.582e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3806901
sample estimates:
Sorensen dissimilarity
0.3390192
attr(,"se")
standard error
0.02533414
$Vogelstein$sanger
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -21.248, p-value < 2.2e-16
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.1415942
sample estimates:
Sorensen dissimilarity
0.1161826
attr(,"se")
standard error
0.01544915
$waldman
$waldman$atlas
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -8.5662, p-value < 2.2e-16
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3121232
sample estimates:
Sorensen dissimilarity
0.280677
attr(,"se")
standard error
0.01911793
$waldman$cangenes
No test performed due not finite (d - d0) / se statistic
data: tab
(d - d0) / se = Inf, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
1
attr(,"se")
standard error
0
$waldman$cis
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = 5.4447, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6704794
sample estimates:
Sorensen dissimilarity
0.6180371
attr(,"se")
standard error
0.03188266
$waldman$miscellaneous
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -10.523, p-value < 2.2e-16
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.2618917
sample estimates:
Sorensen dissimilarity
0.2280702
attr(,"se")
standard error
0.02056206
$waldman$sanger
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -4.9774, p-value = 3.222e-07
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3658088
sample estimates:
Sorensen dissimilarity
0.3269962
attr(,"se")
standard error
0.02359637
$waldman$Vogelstein
Normal asymptotic test for 2x2 contingency tables based on the
Sorensen-Dice dissimilarity
data: tab
(d - d0) / se = -6.6979, p-value = 1.057e-11
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3321681
sample estimates:
Sorensen dissimilarity
0.2956204
attr(,"se")
standard error
0.02221937
attr(,"class")
[1] "equivSDhtestList" "list"
> class(allTests)
[1] "equivSDhtestList" "list"
> set.seed(123)
> allBootTests <- equivTestSorensen(cont_all_BP4, boot = TRUE)
> allBootTests
$cangenes
$cangenes$atlas
No test performed due not finite (d - d0) / se statistic
data: tab
(d - d0) / se = Inf, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
1
attr(,"se")
standard error
0
$cis
$cis$atlas
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = 9.3376, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.7400086
sample estimates:
Sorensen dissimilarity
0.6963563
attr(,"se")
standard error
0.02697813
$cis$cangenes
No test performed due not finite (d - d0) / se statistic
data: tab
(d - d0) / se = Inf, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
1
attr(,"se")
standard error
0
$miscellaneous
$miscellaneous$atlas
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -2.208, p-value = 0.0164
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.000000 0.431994
sample estimates:
Sorensen dissimilarity
0.3936508
attr(,"se")
standard error
0.02300482
$miscellaneous$cangenes
No test performed due not finite (d - d0) / se statistic
data: tab
(d - d0) / se = Inf, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
1
attr(,"se")
standard error
0
$miscellaneous$cis
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = 2.9448, p-value = 0.9974
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6097785
sample estimates:
Sorensen dissimilarity
0.5503356
attr(,"se")
standard error
0.03595877
$sanger
$sanger$atlas
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -3.1077, p-value = 0.0017
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.000000 0.412172
sample estimates:
Sorensen dissimilarity
0.3748056
attr(,"se")
standard error
0.02240875
$sanger$cangenes
No test performed due not finite (d - d0) / se statistic
data: tab
(d - d0) / se = Inf, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
1
attr(,"se")
standard error
0
$sanger$cis
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = 4.0855, p-value = 0.9999
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6467971
sample estimates:
Sorensen dissimilarity
0.5884244
attr(,"se")
standard error
0.03524148
$sanger$miscellaneous
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -5.5254, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3498904
sample estimates:
Sorensen dissimilarity
0.3064877
attr(,"se")
standard error
0.02496764
$Vogelstein
$Vogelstein$atlas
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -4.5244, p-value = 2e-04
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3828507
sample estimates:
Sorensen dissimilarity
0.3473684
attr(,"se")
standard error
0.0214559
$Vogelstein$cangenes
No test performed due not finite (d - d0) / se statistic
data: tab
(d - d0) / se = Inf, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
1
attr(,"se")
standard error
0
$Vogelstein$cis
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = 5.2254, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6775108
sample estimates:
Sorensen dissimilarity
0.6216216
attr(,"se")
standard error
0.03390663
$Vogelstein$miscellaneous
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -4.1614, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3818835
sample estimates:
Sorensen dissimilarity
0.3390192
attr(,"se")
standard error
0.02533414
$Vogelstein$sanger
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -21.248, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.1438618
sample estimates:
Sorensen dissimilarity
0.1161826
attr(,"se")
standard error
0.01544915
$waldman
$waldman$atlas
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -8.5662, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.000000 0.313061
sample estimates:
Sorensen dissimilarity
0.280677
attr(,"se")
standard error
0.01911793
$waldman$cangenes
No test performed due not finite (d - d0) / se statistic
data: tab
(d - d0) / se = Inf, p-value = NA
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0 NaN
sample estimates:
Sorensen dissimilarity
1
attr(,"se")
standard error
0
$waldman$cis
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = 5.4447, p-value = 1
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.6710143
sample estimates:
Sorensen dissimilarity
0.6180371
attr(,"se")
standard error
0.03188266
$waldman$miscellaneous
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -10.523, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.2638861
sample estimates:
Sorensen dissimilarity
0.2280702
attr(,"se")
standard error
0.02056206
$waldman$sanger
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -4.9774, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.0000000 0.3668027
sample estimates:
Sorensen dissimilarity
0.3269962
attr(,"se")
standard error
0.02359637
$waldman$Vogelstein
Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice
dissimilarity (10000 bootstrap replicates)
data: tab
(d - d0) / se = -6.6979, p-value = 9.999e-05
alternative hypothesis: true equivalence limit d0 is less than 0.4444444
95 percent confidence interval:
0.000000 0.334067
sample estimates:
Sorensen dissimilarity
0.2956204
attr(,"se")
standard error
0.02221937
attr(,"class")
[1] "equivSDhtestList" "list"
> class(allBootTests)
[1] "equivSDhtestList" "list"
> getPvalue(allBootTests, simplify = FALSE)
atlas cangenes cis miscellaneous sanger Vogelstein
atlas 0.00000000 NaN 1.0000000 0.01639836 0.00169983 0.00019998
cangenes NaN 0 NaN NaN NaN NaN
cis 1.00000000 NaN 0.0000000 0.99740026 0.99990001 1.00000000
miscellaneous 0.01639836 NaN 0.9974003 0.00000000 0.00009999 0.00009999
sanger 0.00169983 NaN 0.9999000 0.00009999 0.00000000 0.00009999
Vogelstein 0.00019998 NaN 1.0000000 0.00009999 0.00009999 0.00000000
waldman 0.00009999 NaN 1.0000000 0.00009999 0.00009999 0.00009999
waldman
atlas 9.999e-05
cangenes NaN
cis 1.000e+00
miscellaneous 9.999e-05
sanger 9.999e-05
Vogelstein 9.999e-05
waldman 0.000e+00
> getEffNboot(allBootTests)
cangenes.atlas cis.atlas cis.cangenes
NaN 10000 NaN
miscellaneous.atlas miscellaneous.cangenes miscellaneous.cis
10000 NaN 10000
sanger.atlas sanger.cangenes sanger.cis
10000 NaN 10000
sanger.miscellaneous Vogelstein.atlas Vogelstein.cangenes
10000 10000 NaN
Vogelstein.cis Vogelstein.miscellaneous Vogelstein.sanger
10000 10000 10000
waldman.atlas waldman.cangenes waldman.cis
10000 NaN 10000
waldman.miscellaneous waldman.sanger waldman.Vogelstein
10000 10000 10000
>
> # To adjust for testing multiplicity:
> p.adjust(getPvalue(allBootTests), method = "holm")
cangenes.atlas.p-value cis.atlas.p-value
NaN 1.00000000
cis.cangenes.p-value miscellaneous.atlas.p-value
NaN 0.09839016
miscellaneous.cangenes.p-value miscellaneous.cis.p-value
NaN 1.00000000
sanger.atlas.p-value sanger.cangenes.p-value
0.01189881 NaN
sanger.cis.p-value sanger.miscellaneous.p-value
1.00000000 0.00149985
Vogelstein.atlas.p-value Vogelstein.cangenes.p-value
0.00159984 NaN
Vogelstein.cis.p-value Vogelstein.miscellaneous.p-value
1.00000000 0.00149985
Vogelstein.sanger.p-value waldman.atlas.p-value
0.00149985 0.00149985
waldman.cangenes.p-value waldman.cis.p-value
NaN 1.00000000
waldman.miscellaneous.p-value waldman.sanger.p-value
0.00149985 0.00149985
waldman.Vogelstein.p-value
0.00149985
>
> # If only partial statistics are desired:
> dSorensen(cont_all_BP4)
atlas cangenes cis miscellaneous sanger Vogelstein
atlas 0.0000000 1 0.6963563 0.3936508 0.3748056 0.3473684
cangenes 1.0000000 0 1.0000000 1.0000000 1.0000000 1.0000000
cis 0.6963563 1 0.0000000 0.5503356 0.5884244 0.6216216
miscellaneous 0.3936508 1 0.5503356 0.0000000 0.3064877 0.3390192
sanger 0.3748056 1 0.5884244 0.3064877 0.0000000 0.1161826
Vogelstein 0.3473684 1 0.6216216 0.3390192 0.1161826 0.0000000
waldman 0.2806770 1 0.6180371 0.2280702 0.3269962 0.2956204
waldman
atlas 0.2806770
cangenes 1.0000000
cis 0.6180371
miscellaneous 0.2280702
sanger 0.3269962
Vogelstein 0.2956204
waldman 0.0000000
> duppSorensen(cont_all_BP4)
atlas cangenes cis miscellaneous sanger Vogelstein
atlas 0.0000000 NaN 0.7407313 0.4314904 0.4116647 0.3826602
cangenes NaN 0 NaN NaN NaN NaN
cis 0.7407313 NaN 0.0000000 0.6094825 0.6463915 0.6773931
miscellaneous 0.4314904 NaN 0.6094825 0.0000000 0.3475558 0.3806901
sanger 0.4116647 NaN 0.6463915 0.3475558 0.0000000 0.1415942
Vogelstein 0.3826602 NaN 0.6773931 0.3806901 0.1415942 0.0000000
waldman 0.3121232 NaN 0.6704794 0.2618917 0.3658088 0.3321681
waldman
atlas 0.3121232
cangenes NaN
cis 0.6704794
miscellaneous 0.2618917
sanger 0.3658088
Vogelstein 0.3321681
waldman 0.0000000
> seSorensen(cont_all_BP4)
atlas cangenes cis miscellaneous sanger
atlas 0.00000000 0 0.02697813 0.02300482 0.02240875
cangenes 0.00000000 0 0.00000000 0.00000000 0.00000000
cis 0.02697813 0 0.00000000 0.03595877 0.03524148
miscellaneous 0.02300482 0 0.03595877 0.00000000 0.02496764
sanger 0.02240875 0 0.03524148 0.02496764 0.00000000
Vogelstein 0.02145590 0 0.03390663 0.02533414 0.01544915
waldman 0.01911793 0 0.03188266 0.02056206 0.02359637
Vogelstein waldman
atlas 0.02145590 0.01911793
cangenes 0.00000000 0.00000000
cis 0.03390663 0.03188266
miscellaneous 0.02533414 0.02056206
sanger 0.01544915 0.02359637
Vogelstein 0.00000000 0.02221937
waldman 0.02221937 0.00000000
>
>
> # Tipically, in a real study it would be interesting to scan tests
> # along some ontologies and levels inside these ontologies:
> # (which obviously will be a quite slow process)
> # gc()
> # set.seed(123)
> # allBootTests_BP_MF_lev4to8 <- allEquivTestSorensen(allOncoGeneLists,
> # boot = TRUE,
> # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db",
> # ontos = c("BP", "MF"), GOLevels = 4:8)
> # getPvalue(allBootTests_BP_MF_lev4to8)
> # getEffNboot(allBootTests_BP_MF_lev4to8)
>
> proc.time()
user system elapsed
217.92 23.37 242.15
goSorensen.Rcheck/goSorensen-Ex.timings
| name | user | system | elapsed | |
| allBuildEnrichTable | 0 | 0 | 0 | |
| allEquivTestSorensen | 0.34 | 0.03 | 0.38 | |
| allHclustThreshold | 0.07 | 0.02 | 0.10 | |
| allSorenThreshold | 0.07 | 0.00 | 0.06 | |
| buildEnrichTable | 72.39 | 4.46 | 76.91 | |
| dSorensen | 0.09 | 0.04 | 1.00 | |
| duppSorensen | 0.17 | 0.07 | 0.25 | |
| enrichedIn | 63.71 | 4.96 | 68.72 | |
| equivTestSorensen | 0.51 | 0.04 | 0.56 | |
| getDissimilarity | 0.36 | 0.08 | 0.47 | |
| getEffNboot | 1.38 | 0.03 | 1.41 | |
| getNboot | 1.54 | 0.11 | 1.65 | |
| getPvalue | 0.32 | 0.17 | 0.49 | |
| getSE | 0.31 | 0.10 | 0.40 | |
| getTable | 0.28 | 0.23 | 0.52 | |
| getUpper | 0.34 | 0.14 | 0.48 | |
| hclustThreshold | 0.30 | 0.05 | 0.35 | |
| nice2x2Table | 0.00 | 0.02 | 0.01 | |
| seSorensen | 0 | 0 | 0 | |
| sorenThreshold | 0.27 | 0.04 | 0.31 | |
| upgrade | 1.18 | 0.16 | 1.35 | |