Back to Multiple platform build/check report for BioC 3.20: simplified long |
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This page was generated on 2024-12-23 12:05 -0500 (Mon, 23 Dec 2024).
Hostname | OS | Arch (*) | R version | Installed pkgs |
---|---|---|---|---|
nebbiolo2 | Linux (Ubuntu 24.04.1 LTS) | x86_64 | 4.4.2 (2024-10-31) -- "Pile of Leaves" | 4744 |
palomino8 | Windows Server 2022 Datacenter | x64 | 4.4.2 (2024-10-31 ucrt) -- "Pile of Leaves" | 4487 |
merida1 | macOS 12.7.5 Monterey | x86_64 | 4.4.2 (2024-10-31) -- "Pile of Leaves" | 4515 |
kjohnson1 | macOS 13.6.6 Ventura | arm64 | 4.4.2 (2024-10-31) -- "Pile of Leaves" | 4467 |
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 888/2289 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
goSorensen 1.8.0 (landing page) Pablo Flores
| nebbiolo2 | Linux (Ubuntu 24.04.1 LTS) / x86_64 | OK | OK | OK | |||||||||
palomino8 | Windows Server 2022 Datacenter / x64 | OK | OK | OK | OK | |||||||||
merida1 | macOS 12.7.5 Monterey / x86_64 | OK | OK | OK | OK | |||||||||
kjohnson1 | macOS 13.6.6 Ventura / arm64 | OK | 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: 1.8.0 |
Command: F:\biocbuild\bbs-3.20-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:goSorensen.install-out.txt --library=F:\biocbuild\bbs-3.20-bioc\R\library --no-vignettes --timings goSorensen_1.8.0.tar.gz |
StartedAt: 2024-12-20 02:10:16 -0500 (Fri, 20 Dec 2024) |
EndedAt: 2024-12-20 02:21:17 -0500 (Fri, 20 Dec 2024) |
EllapsedTime: 661.2 seconds |
RetCode: 0 |
Status: OK |
CheckDir: goSorensen.Rcheck |
Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### F:\biocbuild\bbs-3.20-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:goSorensen.install-out.txt --library=F:\biocbuild\bbs-3.20-bioc\R\library --no-vignettes --timings goSorensen_1.8.0.tar.gz ### ############################################################################## ############################################################################## * using log directory 'F:/biocbuild/bbs-3.20-bioc/meat/goSorensen.Rcheck' * using R version 4.4.2 (2024-10-31 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 '1.8.0' * 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 installed files from 'inst/doc' ... OK * checking files in 'vignettes' ... OK * checking examples ... OK Examples with CPU (user + system) or elapsed time > 5s user system elapsed hclustThreshold 192.78 15.45 209.84 buildEnrichTable 52.99 3.75 58.15 enrichedIn 44.74 3.13 48.03 * checking for unstated dependencies in 'tests' ... OK * checking tests ... Running 'test_gosorensen_funcs.R' OK * checking for unstated dependencies in vignettes ... NOTE 'library' or 'require' calls not declared from: 'GO.db' 'ggplot2' 'ggrepel' * 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: 1 NOTE See 'F:/biocbuild/bbs-3.20-bioc/meat/goSorensen.Rcheck/00check.log' for details.
goSorensen.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### F:\biocbuild\bbs-3.20-bioc\R\bin\R.exe CMD INSTALL goSorensen ### ############################################################################## ############################################################################## * installing to library 'F:/biocbuild/bbs-3.20-bioc/R/library' * installing *source* package 'goSorensen' ... ** using staged installation ** R ** data ** inst ** 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 version 4.4.2 (2024-10-31 ucrt) -- "Pile of Leaves" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 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(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(tab_atlas.sanger_BP3) > tab_atlas.sanger_BP3 Enriched in sanger Enriched in atlas TRUE FALSE TRUE 38 31 FALSE 2 452 > ?tab_atlas.sanger_BP3 > class(tab_atlas.sanger_BP3) [1] "table" > > # Sorensen-Dice dissimilarity on this contingency table: > ?dSorensen > dSorensen(tab_atlas.sanger_BP3) [1] 0.3027523 > > # Standard error of this Sorensen-Dice dissimilarity estimate: > ?seSorensen > seSorensen(tab_atlas.sanger_BP3) [1] 0.05058655 > > # Upper 95% confidence limit for the Sorensen-Dice dissimilarity: > ?duppSorensen > duppSorensen(tab_atlas.sanger_BP3) [1] 0.3859598 > # 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(tab_atlas.sanger_BP3[1:3]) - 2 > duppSorensen(tab_atlas.sanger_BP3, z.conf.level = qt(1 - 0.95, df)) [1] 0.3870921 > > # Upper confidence limit but using a bootstrap approximation > # to the sampling distribution, instead of a N(0,1) > set.seed(123) > duppSorensen(tab_atlas.sanger_BP3, boot = TRUE) [1] 0.3941622 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(tab_atlas.sanger_BP3) > equiv.atlas.sanger Normal asymptotic test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity data: tab_atlas.sanger_BP3 (d - d0) / se = -2.801, p-value = 0.002547 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3859598 sample estimates: Sorensen dissimilarity 0.3027523 attr(,"se") standard error 0.05058655 > getTable(equiv.atlas.sanger) Enriched in sanger Enriched in atlas TRUE FALSE TRUE 38 31 FALSE 2 452 > getPvalue(equiv.atlas.sanger) p-value 0.002547349 > > 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 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, intersect, is.unsorted, lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind, rownames, sapply, saveRDS, setdiff, table, tapply, union, 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 = -7.6163, p-value = 1.306e-14 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3604408 sample estimates: Sorensen dissimilarity 0.3373016 attr(,"se") standard error 0.01406763 > > # 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 = -7.6163, p-value = 9.999e-05 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3604667 sample estimates: Sorensen dissimilarity 0.3373016 attr(,"se") standard error 0.01406763 > # 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 501 458 FALSE 52 9463 > > # (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 501 458 FALSE 52 9463 > > tst <- equivTestSorensen(tab) > tst Normal asymptotic test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity data: tab (d - d0) / se = -7.6163, p-value = 1.306e-14 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3604408 sample estimates: Sorensen dissimilarity 0.3373016 attr(,"se") standard error 0.01406763 > 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 = -7.6163, p-value = 9.999e-05 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3604667 sample estimates: Sorensen dissimilarity 0.3373016 attr(,"se") standard error 0.01406763 > > dSorensen(tab) [1] 0.3373016 > seSorensen(tab) [1] 0.01406763 > # or: > getDissimilarity(tst) Sorensen dissimilarity 0.3373016 attr(,"se") standard error 0.01406763 > > duppSorensen(tab) [1] 0.3604408 > getUpper(tst) dUpper 0.3604408 > > set.seed(123) > duppSorensen(tab, boot = TRUE) [1] 0.3604667 attr(,"eff.nboot") [1] 10000 > getUpper(bootTst) dUpper 0.3604667 > > # 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 faster: build all tables computing in parallel: > # allPairDiss <- dSorensen(allOncoGeneLists, > # onto = "BP", GOLevel = 5, > # geneUniverse = humanEntrezIDs, orgPackg = "org.Hs.eg.db", > # parallel = TRUE) > # allPairDiss > > # 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(allTabsBP.4) > allTabsBP.4 $cangenes $cangenes$atlas Enriched in atlas Enriched in cangenes TRUE FALSE TRUE 0 0 FALSE 420 3383 attr(,"onto") [1] "BP" attr(,"GOLevel") [1] 4 $cis $cis$atlas Enriched in atlas Enriched in cis TRUE FALSE TRUE 80 3 FALSE 340 3380 $cis$cangenes Enriched in cangenes Enriched in cis TRUE FALSE TRUE 0 83 FALSE 0 3720 attr(,"onto") [1] "BP" attr(,"GOLevel") [1] 4 $miscellaneous $miscellaneous$atlas Enriched in atlas Enriched in miscellaneous TRUE FALSE TRUE 198 21 FALSE 222 3362 $miscellaneous$cangenes Enriched in cangenes Enriched in miscellaneous TRUE FALSE TRUE 0 219 FALSE 0 3584 attr(,"onto") [1] "BP" attr(,"GOLevel") [1] 4 $miscellaneous$cis Enriched in cis Enriched in miscellaneous TRUE FALSE TRUE 70 149 FALSE 13 3571 $sanger $sanger$atlas Enriched in atlas Enriched in sanger TRUE FALSE TRUE 209 24 FALSE 211 3359 $sanger$cangenes Enriched in cangenes Enriched in sanger TRUE FALSE TRUE 0 233 FALSE 0 3570 attr(,"onto") [1] "BP" attr(,"GOLevel") [1] 4 $sanger$cis Enriched in cis Enriched in sanger TRUE FALSE TRUE 68 165 FALSE 15 3555 $sanger$miscellaneous Enriched in miscellaneous Enriched in sanger TRUE FALSE TRUE 151 82 FALSE 68 3502 $Vogelstein $Vogelstein$atlas Enriched in atlas Enriched in Vogelstein TRUE FALSE TRUE 220 32 FALSE 200 3351 $Vogelstein$cangenes Enriched in cangenes Enriched in Vogelstein TRUE FALSE TRUE 0 252 FALSE 0 3551 attr(,"onto") [1] "BP" attr(,"GOLevel") [1] 4 $Vogelstein$cis Enriched in cis Enriched in Vogelstein TRUE FALSE TRUE 68 184 FALSE 15 3536 $Vogelstein$miscellaneous Enriched in miscellaneous Enriched in Vogelstein TRUE FALSE TRUE 156 96 FALSE 63 3488 $Vogelstein$sanger Enriched in sanger Enriched in Vogelstein TRUE FALSE TRUE 217 35 FALSE 16 3535 $waldman $waldman$atlas Enriched in atlas Enriched in waldman TRUE FALSE TRUE 264 39 FALSE 156 3344 $waldman$cangenes Enriched in cangenes Enriched in waldman TRUE FALSE TRUE 0 303 FALSE 0 3500 attr(,"onto") [1] "BP" attr(,"GOLevel") [1] 4 $waldman$cis Enriched in cis Enriched in waldman TRUE FALSE TRUE 77 226 FALSE 6 3494 $waldman$miscellaneous Enriched in miscellaneous Enriched in waldman TRUE FALSE TRUE 203 100 FALSE 16 3484 $waldman$sanger Enriched in sanger Enriched in waldman TRUE FALSE TRUE 181 122 FALSE 52 3448 $waldman$Vogelstein Enriched in Vogelstein Enriched in waldman TRUE FALSE TRUE 192 111 FALSE 60 3440 attr(,"onto") [1] "BP" attr(,"GOLevel") [1] 4 attr(,"class") [1] "tableList" "list" > class(allTabsBP.4) [1] "tableList" "list" > # 2) Then perform all required computatios from these enrichment contingency tables... > # All pairwise tests: > allTests <- equivTestSorensen(allTabsBP.4) > 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 = 8.807, p-value = 1 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.7262589 sample estimates: Sorensen dissimilarity 0.6819085 attr(,"se") standard error 0.02696312 $cis$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 = -2.8406, p-value = 0.002252 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.4174355 sample estimates: Sorensen dissimilarity 0.3802817 attr(,"se") standard error 0.02258792 $miscellaneous$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 = 2.5804, p-value = 0.9951 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.5950555 sample estimates: Sorensen dissimilarity 0.5364238 attr(,"se") standard error 0.03564549 $sanger $sanger$atlas Normal asymptotic test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity data: tab (d - d0) / se = -3.8566, p-value = 5.748e-05 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3959452 sample estimates: Sorensen dissimilarity 0.3598775 attr(,"se") standard error 0.02192764 $sanger$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 = 3.5799, p-value = 0.9998 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.6271347 sample estimates: Sorensen dissimilarity 0.5696203 attr(,"se") standard error 0.03496631 $sanger$miscellaneous Normal asymptotic test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity data: tab (d - d0) / se = -4.3974, p-value = 5.479e-06 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3739718 sample estimates: Sorensen dissimilarity 0.3318584 attr(,"se") standard error 0.02560313 $Vogelstein $Vogelstein$atlas Normal asymptotic test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity data: tab (d - d0) / se = -4.6585, p-value = 1.593e-06 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3802668 sample estimates: Sorensen dissimilarity 0.3452381 attr(,"se") standard error 0.02129595 $Vogelstein$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 = 4.4076, p-value = 1 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.6498536 sample estimates: Sorensen dissimilarity 0.5940299 attr(,"se") standard error 0.03393844 $Vogelstein$miscellaneous Normal asymptotic test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity data: tab (d - d0) / se = -4.2339, p-value = 1.148e-05 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3790962 sample estimates: Sorensen dissimilarity 0.3375796 attr(,"se") standard error 0.02524032 $Vogelstein$sanger Normal asymptotic test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity data: tab (d - d0) / se = -23.128, p-value < 2.2e-16 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.1292852 sample estimates: Sorensen dissimilarity 0.1051546 attr(,"se") standard error 0.01467036 $waldman $waldman$atlas Normal asymptotic test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity data: tab (d - d0) / se = -9.3848, p-value < 2.2e-16 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3003348 sample estimates: Sorensen dissimilarity 0.2697095 attr(,"se") standard error 0.01861884 $waldman$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 = 4.9573, p-value = 1 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.6529946 sample estimates: Sorensen dissimilarity 0.6010363 attr(,"se") standard error 0.03158842 $waldman$miscellaneous Normal asymptotic test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity data: tab (d - d0) / se = -11.029, p-value < 2.2e-16 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.2553636 sample estimates: Sorensen dissimilarity 0.2222222 attr(,"se") standard error 0.02014852 $waldman$sanger Normal asymptotic test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity data: tab (d - d0) / se = -5.1402, p-value = 1.372e-07 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3629683 sample estimates: Sorensen dissimilarity 0.3246269 attr(,"se") standard error 0.02330993 $waldman$Vogelstein Normal asymptotic test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity data: tab (d - d0) / se = -6.0739, p-value = 6.243e-10 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.000000 0.345029 sample estimates: Sorensen dissimilarity 0.3081081 attr(,"se") standard error 0.02244631 attr(,"class") [1] "equivSDhtestList" "list" > class(allTests) [1] "equivSDhtestList" "list" > set.seed(123) > allBootTests <- equivTestSorensen(allTabsBP.4, 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 = 8.807, p-value = 1 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.000000 0.725535 sample estimates: Sorensen dissimilarity 0.6819085 attr(,"se") standard error 0.02696312 $cis$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 = -2.8406, p-value = 0.004 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.000000 0.418077 sample estimates: Sorensen dissimilarity 0.3802817 attr(,"se") standard error 0.02258792 $miscellaneous$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 = 2.5804, p-value = 0.9933 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.000000 0.595412 sample estimates: Sorensen dissimilarity 0.5364238 attr(,"se") standard error 0.03564549 $sanger $sanger$atlas Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity (10000 bootstrap replicates) data: tab (d - d0) / se = -3.8566, p-value = 3e-04 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3960626 sample estimates: Sorensen dissimilarity 0.3598775 attr(,"se") standard error 0.02192764 $sanger$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 = 3.5799, p-value = 0.9996 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.6278561 sample estimates: Sorensen dissimilarity 0.5696203 attr(,"se") standard error 0.03496631 $sanger$miscellaneous Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity (10000 bootstrap replicates) data: tab (d - d0) / se = -4.3974, p-value = 2e-04 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3765829 sample estimates: Sorensen dissimilarity 0.3318584 attr(,"se") standard error 0.02560313 $Vogelstein $Vogelstein$atlas Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity (10000 bootstrap replicates) data: tab (d - d0) / se = -4.6585, p-value = 2e-04 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3809169 sample estimates: Sorensen dissimilarity 0.3452381 attr(,"se") standard error 0.02129595 $Vogelstein$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 = 4.4076, p-value = 1 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.6489965 sample estimates: Sorensen dissimilarity 0.5940299 attr(,"se") standard error 0.03393844 $Vogelstein$miscellaneous Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity (10000 bootstrap replicates) data: tab (d - d0) / se = -4.2339, p-value = 9.999e-05 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3796934 sample estimates: Sorensen dissimilarity 0.3375796 attr(,"se") standard error 0.02524032 $Vogelstein$sanger Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity (10000 bootstrap replicates) data: tab (d - d0) / se = -23.128, p-value = 9.999e-05 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.1312585 sample estimates: Sorensen dissimilarity 0.1051546 attr(,"se") standard error 0.01467036 $waldman $waldman$atlas Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity (10000 bootstrap replicates) data: tab (d - d0) / se = -9.3848, p-value = 9.999e-05 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3006583 sample estimates: Sorensen dissimilarity 0.2697095 attr(,"se") standard error 0.01861884 $waldman$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 = 4.9573, p-value = 1 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.6525683 sample estimates: Sorensen dissimilarity 0.6010363 attr(,"se") standard error 0.03158842 $waldman$miscellaneous Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity (10000 bootstrap replicates) data: tab (d - d0) / se = -11.029, p-value = 9.999e-05 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.2577849 sample estimates: Sorensen dissimilarity 0.2222222 attr(,"se") standard error 0.02014852 $waldman$sanger Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity (10000 bootstrap replicates) data: tab (d - d0) / se = -5.1402, p-value = 9.999e-05 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3639666 sample estimates: Sorensen dissimilarity 0.3246269 attr(,"se") standard error 0.02330993 $waldman$Vogelstein Bootstrap test for 2x2 contingency tables based on the Sorensen-Dice dissimilarity (10000 bootstrap replicates) data: tab (d - d0) / se = -6.0739, p-value = 9.999e-05 alternative hypothesis: true equivalence limit d0 is less than 0.4444444 95 percent confidence interval: 0.0000000 0.3470915 sample estimates: Sorensen dissimilarity 0.3081081 attr(,"se") standard error 0.02244631 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 0.00399960 0.00029997 0.00019998 cangenes NaN 0 NaN NaN NaN NaN cis 1.00000000 NaN 0.0000000 0.99330067 0.99960004 1.00000000 miscellaneous 0.00399960 NaN 0.9933007 0.00000000 0.00019998 0.00009999 sanger 0.00029997 NaN 0.9996000 0.00019998 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 0.02399760 miscellaneous.cangenes.p-value miscellaneous.cis.p-value NaN 1.00000000 sanger.atlas.p-value sanger.cangenes.p-value 0.00209979 NaN sanger.cis.p-value sanger.miscellaneous.p-value 1.00000000 0.00179982 Vogelstein.atlas.p-value Vogelstein.cangenes.p-value 0.00179982 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(allTabsBP.4) atlas cangenes cis miscellaneous sanger Vogelstein atlas 0.0000000 1 0.6819085 0.3802817 0.3598775 0.3452381 cangenes 1.0000000 0 1.0000000 1.0000000 1.0000000 1.0000000 cis 0.6819085 1 0.0000000 0.5364238 0.5696203 0.5940299 miscellaneous 0.3802817 1 0.5364238 0.0000000 0.3318584 0.3375796 sanger 0.3598775 1 0.5696203 0.3318584 0.0000000 0.1051546 Vogelstein 0.3452381 1 0.5940299 0.3375796 0.1051546 0.0000000 waldman 0.2697095 1 0.6010363 0.2222222 0.3246269 0.3081081 waldman atlas 0.2697095 cangenes 1.0000000 cis 0.6010363 miscellaneous 0.2222222 sanger 0.3246269 Vogelstein 0.3081081 waldman 0.0000000 > duppSorensen(allTabsBP.4) atlas cangenes cis miscellaneous sanger Vogelstein atlas 0.0000000 NaN 0.7262589 0.4174355 0.3959452 0.3802668 cangenes NaN 0 NaN NaN NaN NaN cis 0.7262589 NaN 0.0000000 0.5950555 0.6271347 0.6498536 miscellaneous 0.4174355 NaN 0.5950555 0.0000000 0.3739718 0.3790962 sanger 0.3959452 NaN 0.6271347 0.3739718 0.0000000 0.1292852 Vogelstein 0.3802668 NaN 0.6498536 0.3790962 0.1292852 0.0000000 waldman 0.3003348 NaN 0.6529946 0.2553636 0.3629683 0.3450290 waldman atlas 0.3003348 cangenes NaN cis 0.6529946 miscellaneous 0.2553636 sanger 0.3629683 Vogelstein 0.3450290 waldman 0.0000000 > seSorensen(allTabsBP.4) atlas cangenes cis miscellaneous sanger atlas 0.00000000 0 0.02696312 0.02258792 0.02192764 cangenes 0.00000000 0 0.00000000 0.00000000 0.00000000 cis 0.02696312 0 0.00000000 0.03564549 0.03496631 miscellaneous 0.02258792 0 0.03564549 0.00000000 0.02560313 sanger 0.02192764 0 0.03496631 0.02560313 0.00000000 Vogelstein 0.02129595 0 0.03393844 0.02524032 0.01467036 waldman 0.01861884 0 0.03158842 0.02014852 0.02330993 Vogelstein waldman atlas 0.02129595 0.01861884 cangenes 0.00000000 0.00000000 cis 0.03393844 0.03158842 miscellaneous 0.02524032 0.02014852 sanger 0.01467036 0.02330993 Vogelstein 0.00000000 0.02244631 waldman 0.02244631 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 158.67 13.53 175.98
goSorensen.Rcheck/goSorensen-Ex.timings
name | user | system | elapsed | |
allBuildEnrichTable | 0 | 0 | 0 | |
allEquivTestSorensen | 0.35 | 0.02 | 0.38 | |
allHclustThreshold | 0.05 | 0.00 | 0.05 | |
allSorenThreshold | 0.04 | 0.00 | 0.05 | |
buildEnrichTable | 52.99 | 3.75 | 58.15 | |
dSorensen | 0.13 | 0.06 | 1.56 | |
duppSorensen | 0.11 | 0.09 | 0.20 | |
enrichedIn | 44.74 | 3.13 | 48.03 | |
equivTestSorensen | 0.55 | 0.05 | 0.59 | |
getDissimilarity | 0.31 | 0.17 | 0.52 | |
getEffNboot | 1.27 | 0.06 | 1.33 | |
getNboot | 1.55 | 0.05 | 1.61 | |
getPvalue | 0.42 | 0.07 | 0.50 | |
getSE | 0.30 | 0.18 | 0.47 | |
getTable | 0.34 | 0.25 | 0.59 | |
getUpper | 0.36 | 0.12 | 0.50 | |
hclustThreshold | 192.78 | 15.45 | 209.84 | |
nice2x2Table | 0 | 0 | 0 | |
seSorensen | 0.00 | 0.00 | 0.03 | |
sorenThreshold | 0.30 | 0.05 | 0.36 | |
upgrade | 1.25 | 0.27 | 3.11 | |