Back to Multiple platform build/check report for BioC 3.20: simplified long |
|
This page was generated on 2024-11-09 21:30 -0500 (Sat, 09 Nov 2024).
Hostname | OS | Arch (*) | R version | Installed pkgs |
---|---|---|---|---|
teran2 | Linux (Ubuntu 24.04.1 LTS) | x86_64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4505 |
palomino8 | Windows Server 2022 Datacenter | x64 | 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life" | 4506 |
lconway | macOS 12.7.1 Monterey | x86_64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4538 |
kjohnson3 | macOS 13.6.5 Ventura | arm64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4486 |
kunpeng2 | Linux (openEuler 22.03 LTS-SP1) | aarch64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4493 |
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 632/2289 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
edgeR 4.4.0 (landing page) Yunshun Chen
| teran2 | Linux (Ubuntu 24.04.1 LTS) / x86_64 | OK | OK | OK | |||||||||
palomino8 | Windows Server 2022 Datacenter / x64 | OK | OK | OK | OK | |||||||||
lconway | macOS 12.7.1 Monterey / x86_64 | OK | OK | OK | OK | |||||||||
kjohnson3 | macOS 13.6.5 Ventura / arm64 | OK | OK | OK | OK | |||||||||
kunpeng2 | Linux (openEuler 22.03 LTS-SP1) / aarch64 | OK | OK | OK | ||||||||||
To the developers/maintainers of the edgeR package: - Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/edgeR.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: edgeR |
Version: 4.4.0 |
Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:edgeR.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings edgeR_4.4.0.tar.gz |
StartedAt: 2024-11-09 01:31:24 -0500 (Sat, 09 Nov 2024) |
EndedAt: 2024-11-09 01:35:36 -0500 (Sat, 09 Nov 2024) |
EllapsedTime: 251.5 seconds |
RetCode: 0 |
Status: OK |
CheckDir: edgeR.Rcheck |
Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:edgeR.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings edgeR_4.4.0.tar.gz ### ############################################################################## ############################################################################## * using log directory ‘/Users/biocbuild/bbs-3.20-bioc/meat/edgeR.Rcheck’ * using R version 4.4.1 (2024-06-14) * using platform: aarch64-apple-darwin20 * R was compiled by Apple clang version 14.0.0 (clang-1400.0.29.202) GNU Fortran (GCC) 12.2.0 * running under: macOS Ventura 13.6.7 * using session charset: UTF-8 * using option ‘--no-vignettes’ * checking for file ‘edgeR/DESCRIPTION’ ... OK * this is package ‘edgeR’ version ‘4.4.0’ * checking package namespace information ... OK * checking package dependencies ...Warning: unable to access index for repository https://CRAN.R-project.org/src/contrib: cannot open URL 'https://CRAN.R-project.org/src/contrib/PACKAGES' 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 for sufficient/correct file permissions ... OK * checking whether package ‘edgeR’ can be installed ... OK * used C compiler: ‘Apple clang version 15.0.0 (clang-1500.1.0.2.5)’ * used SDK: ‘MacOSX11.3.sdk’ * 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 whether startup messages can be suppressed ... 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 line endings in C/C++/Fortran sources/headers ... OK * checking line endings in Makefiles ... OK * checking compilation flags in Makevars ... OK * checking for GNU extensions in Makefiles ... OK * checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK * checking use of PKG_*FLAGS in Makefiles ... OK * checking compiled code ... NOTE Note: information on .o files is not available * checking sizes of PDF files under ‘inst/doc’ ... OK * checking files in ‘vignettes’ ... OK * checking examples ... OK * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... Running ‘edgeR-Tests.R’ Comparing ‘edgeR-Tests.Rout’ to ‘edgeR-Tests.Rout.save’ ... OK 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: 1 NOTE See ‘/Users/biocbuild/bbs-3.20-bioc/meat/edgeR.Rcheck/00check.log’ for details.
edgeR.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL edgeR ### ############################################################################## ############################################################################## * installing to library ‘/Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library’ * installing *source* package ‘edgeR’ ... ** using staged installation ** libs using C compiler: ‘Apple clang version 15.0.0 (clang-1500.1.0.2.5)’ using SDK: ‘MacOSX11.3.sdk’ clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c R_exports.c -o R_exports.o clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c R_process_hairpin_reads.c -o R_process_hairpin_reads.o clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c add_prior_count.c -o add_prior_count.o clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c clowess.c -o clowess.o clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c compute_apl.c -o compute_apl.o clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c compute_cpm.c -o compute_cpm.o clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c compute_nbdev.c -o compute_nbdev.o clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c exact_test_by_dev.c -o exact_test_by_dev.o clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c fmm_spline.c -o fmm_spline.o clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c glm.c -o glm.o clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c good_turing.c -o good_turing.o clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c init.c -o init.o clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c interpolator.c -o interpolator.o clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c loess_by_col.c -o loess_by_col.o clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c object.c -o object.o clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c ql_glm.c -o ql_glm.o clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/arm64/include -fPIC -falign-functions=64 -Wall -g -O2 -c ql_weights.c -o ql_weights.o clang -arch arm64 -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -L/Library/Frameworks/R.framework/Resources/lib -L/opt/R/arm64/lib -o edgeR.so R_exports.o R_process_hairpin_reads.o add_prior_count.o clowess.o compute_apl.o compute_cpm.o compute_nbdev.o exact_test_by_dev.o fmm_spline.o glm.o good_turing.o init.o interpolator.o loess_by_col.o object.o ql_glm.o ql_weights.o -L/Library/Frameworks/R.framework/Resources/lib -lRlapack -L/Library/Frameworks/R.framework/Resources/lib -lRblas -L/opt/gfortran/lib/gcc/aarch64-apple-darwin20.0/12.2.0 -L/opt/gfortran/lib -lgfortran -lemutls_w -lquadmath -F/Library/Frameworks/R.framework/.. -framework R -Wl,-framework -Wl,CoreFoundation installing to /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library/00LOCK-edgeR/00new/edgeR/libs ** R ** 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 ** checking absolute paths in shared objects and dynamic libraries ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (edgeR)
edgeR.Rcheck/tests/edgeR-Tests.Rout
R version 4.4.1 (2024-06-14) -- "Race for Your Life" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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(edgeR) Loading required package: limma > options(warnPartialMatchArgs=TRUE,warnPartialMatchAttr=TRUE,warnPartialMatchDollar=TRUE) > > set.seed(0); u <- runif(100) > > # generate raw counts from NB, create list object > y <- matrix(rnbinom(80,size=5,mu=10),nrow=20) > y <- rbind(0,c(0,0,2,2),y) > rownames(y) <- paste("Tag",1:nrow(y),sep=".") > d <- DGEList(counts=y,group=rep(1:2,each=2),lib.size=1001:1004) > > filterByExpr(d) Tag.1 Tag.2 Tag.3 Tag.4 Tag.5 Tag.6 Tag.7 Tag.8 Tag.9 Tag.10 Tag.11 FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE Tag.12 Tag.13 Tag.14 Tag.15 Tag.16 Tag.17 Tag.18 Tag.19 Tag.20 Tag.21 Tag.22 TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE > > # estimate common dispersion and find differences in expression > d <- estimateCommonDisp(d) > d$common.dispersion [1] 0.210292 > de <- exactTest(d) > summary(de$table) logFC logCPM PValue Min. :-1.7266 Min. :10.96 Min. :0.01976 1st Qu.:-0.4855 1st Qu.:13.21 1st Qu.:0.33120 Median : 0.2253 Median :13.37 Median :0.56514 Mean : 0.1877 Mean :13.26 Mean :0.54504 3rd Qu.: 0.5258 3rd Qu.:13.70 3rd Qu.:0.81052 Max. : 4.0861 Max. :14.31 Max. :1.00000 > topTags(de) Comparison of groups: 2-1 logFC logCPM PValue FDR Tag.17 2.0450964 13.73726 0.01975954 0.4347099 Tag.21 -1.7265870 13.38327 0.06131012 0.6744114 Tag.6 -1.6329986 12.81479 0.12446044 0.8982100 Tag.2 4.0861092 11.54121 0.16331090 0.8982100 Tag.16 0.9324996 13.57074 0.29050785 0.9655885 Tag.20 0.8543138 13.76364 0.31736609 0.9655885 Tag.12 0.7081170 14.31389 0.37271028 0.9655885 Tag.19 -0.7976602 13.31405 0.40166354 0.9655885 Tag.3 -0.7300410 13.54155 0.42139935 0.9655885 Tag.8 -0.7917906 12.86353 0.47117217 0.9655885 > > d2 <- estimateTagwiseDisp(d,trend="none",prior.df=20) > summary(d2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1757 0.1896 0.1989 0.2063 0.2185 0.2677 > de <- exactTest(d2,dispersion="common") > topTags(de) Comparison of groups: 2-1 logFC logCPM PValue FDR Tag.17 2.0450964 13.73726 0.01975954 0.4347099 Tag.21 -1.7265870 13.38327 0.06131012 0.6744114 Tag.6 -1.6329986 12.81479 0.12446044 0.8982100 Tag.2 4.0861092 11.54121 0.16331090 0.8982100 Tag.16 0.9324996 13.57074 0.29050785 0.9655885 Tag.20 0.8543138 13.76364 0.31736609 0.9655885 Tag.12 0.7081170 14.31389 0.37271028 0.9655885 Tag.19 -0.7976602 13.31405 0.40166354 0.9655885 Tag.3 -0.7300410 13.54155 0.42139935 0.9655885 Tag.8 -0.7917906 12.86353 0.47117217 0.9655885 > > de <- exactTest(d2) > topTags(de) Comparison of groups: 2-1 logFC logCPM PValue FDR Tag.17 2.0450987 13.73726 0.01327001 0.2919403 Tag.21 -1.7265897 13.38327 0.05683886 0.6252275 Tag.6 -1.6329910 12.81479 0.11460208 0.8404152 Tag.2 4.0861092 11.54121 0.16126207 0.8869414 Tag.16 0.9324975 13.57074 0.28103256 0.9669238 Tag.20 0.8543178 13.76364 0.30234789 0.9669238 Tag.12 0.7081149 14.31389 0.37917895 0.9669238 Tag.19 -0.7976633 13.31405 0.40762735 0.9669238 Tag.3 -0.7300478 13.54155 0.40856822 0.9669238 Tag.8 -0.7918243 12.86353 0.49005179 0.9669238 > > d2 <- estimateTagwiseDisp(d,trend="movingave",span=0.4,prior.df=20) > summary(d2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1005 0.1629 0.2064 0.2077 0.2585 0.3164 > de <- exactTest(d2) > topTags(de) Comparison of groups: 2-1 logFC logCPM PValue FDR Tag.17 2.0450951 13.73726 0.02427872 0.5341319 Tag.21 -1.7265927 13.38327 0.05234833 0.5758316 Tag.6 -1.6330014 12.81479 0.12846308 0.8954397 Tag.2 4.0861092 11.54121 0.16280722 0.8954397 Tag.16 0.9324887 13.57074 0.24308201 0.9711975 Tag.20 0.8543044 13.76364 0.35534649 0.9711975 Tag.19 -0.7976535 13.31405 0.38873717 0.9711975 Tag.3 -0.7300525 13.54155 0.40001438 0.9711975 Tag.12 0.7080985 14.31389 0.43530227 0.9711975 Tag.8 -0.7918376 12.86353 0.49782701 0.9711975 > > summary(exactTest(d2,rejection.region="smallp")$table$PValue) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.02428 0.36369 0.55662 0.54319 0.78889 1.00000 > summary(exactTest(d2,rejection.region="deviance")$table$PValue) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.02428 0.36369 0.55662 0.54319 0.78889 1.00000 > > d2 <- estimateTagwiseDisp(d,trend="loess",span=0.8,prior.df=20) > summary(d2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1165 0.1449 0.1832 0.1848 0.2116 0.2825 > de <- exactTest(d2) > topTags(de) Comparison of groups: 2-1 logFC logCPM PValue FDR Tag.17 2.0450979 13.73726 0.01546795 0.3402949 Tag.21 -1.7266049 13.38327 0.03545446 0.3899990 Tag.6 -1.6329841 12.81479 0.10632987 0.7797524 Tag.2 4.0861092 11.54121 0.16057893 0.8831841 Tag.16 0.9324935 13.57074 0.26348818 0.9658389 Tag.20 0.8543140 13.76364 0.31674090 0.9658389 Tag.19 -0.7976354 13.31405 0.35564858 0.9658389 Tag.3 -0.7300593 13.54155 0.38833737 0.9658389 Tag.12 0.7081041 14.31389 0.41513004 0.9658389 Tag.8 -0.7918152 12.86353 0.48483449 0.9658389 > > d2 <- estimateTagwiseDisp(d,trend="tricube",span=0.8,prior.df=20) > summary(d2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1165 0.1449 0.1832 0.1848 0.2116 0.2825 > de <- exactTest(d2) > topTags(de) Comparison of groups: 2-1 logFC logCPM PValue FDR Tag.17 2.0450979 13.73726 0.01546795 0.3402949 Tag.21 -1.7266049 13.38327 0.03545446 0.3899990 Tag.6 -1.6329841 12.81479 0.10632987 0.7797524 Tag.2 4.0861092 11.54121 0.16057893 0.8831841 Tag.16 0.9324935 13.57074 0.26348818 0.9658389 Tag.20 0.8543140 13.76364 0.31674090 0.9658389 Tag.19 -0.7976354 13.31405 0.35564858 0.9658389 Tag.3 -0.7300593 13.54155 0.38833737 0.9658389 Tag.12 0.7081041 14.31389 0.41513004 0.9658389 Tag.8 -0.7918152 12.86353 0.48483449 0.9658389 > > # mglmOneWay > design <- model.matrix(~group,data=d$samples) > mglmOneWay(d[1:10,],design,dispersion=0.2) $coefficients (Intercept) group2 Tag.1 -1.000000e+08 0.000000e+00 Tag.2 -1.000000e+08 1.000000e+08 Tag.3 2.525729e+00 -5.108256e-01 Tag.4 2.525729e+00 1.484200e-01 Tag.5 2.140066e+00 -1.941560e-01 Tag.6 2.079442e+00 -1.163151e+00 Tag.7 2.014903e+00 2.363888e-01 Tag.8 1.945910e+00 -5.596158e-01 Tag.9 1.504077e+00 2.006707e-01 Tag.10 2.302585e+00 2.623643e-01 $fitted.values Sample1 Sample2 Sample3 Sample4 Tag.1 0.0 0.0 0.0 0.0 Tag.2 0.0 0.0 2.0 2.0 Tag.3 12.5 12.5 7.5 7.5 Tag.4 12.5 12.5 14.5 14.5 Tag.5 8.5 8.5 7.0 7.0 Tag.6 8.0 8.0 2.5 2.5 Tag.7 7.5 7.5 9.5 9.5 Tag.8 7.0 7.0 4.0 4.0 Tag.9 4.5 4.5 5.5 5.5 Tag.10 10.0 10.0 13.0 13.0 > mglmOneWay(d[1:10,],design,dispersion=0) $coefficients (Intercept) group2 Tag.1 -1.000000e+08 0.000000e+00 Tag.2 -1.000000e+08 1.000000e+08 Tag.3 2.525729e+00 -5.108256e-01 Tag.4 2.525729e+00 1.484200e-01 Tag.5 2.140066e+00 -1.941560e-01 Tag.6 2.079442e+00 -1.163151e+00 Tag.7 2.014903e+00 2.363888e-01 Tag.8 1.945910e+00 -5.596158e-01 Tag.9 1.504077e+00 2.006707e-01 Tag.10 2.302585e+00 2.623643e-01 $fitted.values Sample1 Sample2 Sample3 Sample4 Tag.1 0.0 0.0 0.0 0.0 Tag.2 0.0 0.0 2.0 2.0 Tag.3 12.5 12.5 7.5 7.5 Tag.4 12.5 12.5 14.5 14.5 Tag.5 8.5 8.5 7.0 7.0 Tag.6 8.0 8.0 2.5 2.5 Tag.7 7.5 7.5 9.5 9.5 Tag.8 7.0 7.0 4.0 4.0 Tag.9 4.5 4.5 5.5 5.5 Tag.10 10.0 10.0 13.0 13.0 > > fit <- glmFit(d,design,dispersion=d$common.dispersion,prior.count=0.5/4) > lrt <- glmLRT(fit,coef=2) > topTags(lrt) Coefficient: group2 logFC logCPM LR PValue FDR Tag.17 2.0450964 13.73726 6.0485417 0.01391779 0.3058698 Tag.2 4.0861092 11.54121 4.8400340 0.02780635 0.3058698 Tag.21 -1.7265870 13.38327 4.0777825 0.04345065 0.3186381 Tag.6 -1.6329986 12.81479 3.0078205 0.08286364 0.4557500 Tag.16 0.9324996 13.57074 1.3477682 0.24566867 0.8276702 Tag.20 0.8543138 13.76364 1.1890032 0.27553071 0.8276702 Tag.19 -0.7976602 13.31405 0.9279151 0.33540526 0.8276702 Tag.12 0.7081170 14.31389 0.9095513 0.34023349 0.8276702 Tag.3 -0.7300410 13.54155 0.8300307 0.36226364 0.8276702 Tag.8 -0.7917906 12.86353 0.7830377 0.37621371 0.8276702 > > fit <- glmFit(d,design,dispersion=d$common.dispersion,prior.count=0.5) > summary(fit$coefficients) (Intercept) group2 Min. :-7.604 Min. :-1.13681 1st Qu.:-4.895 1st Qu.:-0.32341 Median :-4.713 Median : 0.15083 Mean :-4.940 Mean : 0.07817 3rd Qu.:-4.524 3rd Qu.: 0.35163 Max. :-4.107 Max. : 1.60864 > > fit <- glmFit(d,design,prior.count=0.5/4) > lrt <- glmLRT(fit,coef=2) > topTags(lrt) Coefficient: group2 logFC logCPM LR PValue FDR Tag.17 2.0450964 13.73726 6.0485417 0.01391779 0.3058698 Tag.2 4.0861092 11.54121 4.8400340 0.02780635 0.3058698 Tag.21 -1.7265870 13.38327 4.0777825 0.04345065 0.3186381 Tag.6 -1.6329986 12.81479 3.0078205 0.08286364 0.4557500 Tag.16 0.9324996 13.57074 1.3477682 0.24566867 0.8276702 Tag.20 0.8543138 13.76364 1.1890032 0.27553071 0.8276702 Tag.19 -0.7976602 13.31405 0.9279151 0.33540526 0.8276702 Tag.12 0.7081170 14.31389 0.9095513 0.34023349 0.8276702 Tag.3 -0.7300410 13.54155 0.8300307 0.36226364 0.8276702 Tag.8 -0.7917906 12.86353 0.7830377 0.37621371 0.8276702 > > dglm <- estimateGLMCommonDisp(d,design) > dglm$common.dispersion [1] 0.2033282 > dglm <- estimateGLMTagwiseDisp(dglm,design,prior.df=20) > summary(dglm$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1756 0.1879 0.1998 0.2031 0.2135 0.2578 > fit <- glmFit(dglm,design,prior.count=0.5/4) > lrt <- glmLRT(fit,coef=2) > topTags(lrt) Coefficient: group2 logFC logCPM LR PValue FDR Tag.17 2.0450988 13.73727 6.8001118 0.009115216 0.2005348 Tag.2 4.0861092 11.54122 4.8594088 0.027495756 0.2872068 Tag.21 -1.7265904 13.38327 4.2537154 0.039164558 0.2872068 Tag.6 -1.6329904 12.81479 3.1763761 0.074710253 0.4109064 Tag.16 0.9324970 13.57074 1.4126709 0.234613512 0.8499599 Tag.20 0.8543183 13.76364 1.2721097 0.259371274 0.8499599 Tag.19 -0.7976614 13.31405 0.9190392 0.337727381 0.8499599 Tag.12 0.7081163 14.31389 0.9014515 0.342392806 0.8499599 Tag.3 -0.7300488 13.54155 0.8817937 0.347710872 0.8499599 Tag.8 -0.7918166 12.86353 0.7356185 0.391068049 0.8603497 > dglm <- estimateGLMTrendedDisp(dglm,design) > summary(dglm$trended.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1522 0.1676 0.1740 0.1887 0.2000 0.3469 > dglm <- estimateGLMTrendedDisp(dglm,design,method="power") > summary(dglm$trended.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1522 0.1676 0.1740 0.1887 0.2000 0.3469 > dglm <- estimateGLMTrendedDisp(dglm,design,method="spline") > summary(dglm$trended.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.09353 0.11082 0.15463 0.19006 0.23050 0.52006 > dglm <- estimateGLMTrendedDisp(dglm,design,method="bin.spline") > summary(dglm$trended.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1997 0.1997 0.1997 0.1997 0.1997 0.1997 > dglm <- estimateGLMTagwiseDisp(dglm,design,prior.df=20) > summary(dglm$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1385 0.1792 0.1964 0.1935 0.2026 0.2709 > > dglm2 <- estimateDisp(dglm, design) > summary(dglm2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1652 0.1740 0.1821 0.1852 0.1909 0.2259 > dglm2 <- estimateDisp(dglm, design, prior.df=20) > summary(dglm2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1527 0.1669 0.1814 0.1858 0.1951 0.2497 > dglm2 <- estimateDisp(dglm, design, robust=TRUE) > summary(dglm2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1652 0.1735 0.1822 0.1854 0.1905 0.2280 > > # Continuous trend > nlibs <- 3 > ntags <- 1000 > dispersion.true <- 0.1 > # Make first transcript respond to covariate x > x <- 0:2 > design <- model.matrix(~x) > beta.true <- cbind(Beta1=2,Beta2=c(2,rep(0,ntags-1))) > mu.true <- 2^(beta.true %*% t(design)) > # Generate count data > y <- rnbinom(ntags*nlibs,mu=mu.true,size=1/dispersion.true) > y <- matrix(y,ntags,nlibs) > colnames(y) <- c("x0","x1","x2") > rownames(y) <- paste("Gene",1:ntags,sep="") > d <- DGEList(y) > d <- normLibSizes(d,method="TMM") > fit <- glmFit(d, design, dispersion=dispersion.true, prior.count=0.5/3) > results <- glmLRT(fit, coef=2) > topTags(results) Coefficient: x logFC logCPM LR PValue FDR Gene1 2.907024 13.56183 38.738512 4.845536e-10 4.845536e-07 Gene61 2.855317 10.27136 10.738307 1.049403e-03 5.247015e-01 Gene62 -2.123902 10.53174 8.818703 2.981585e-03 8.334760e-01 Gene134 -1.949073 10.53355 8.125889 4.363759e-03 8.334760e-01 Gene740 -1.610046 10.94907 8.013408 4.643227e-03 8.334760e-01 Gene354 2.022698 10.45066 7.826308 5.149118e-03 8.334760e-01 Gene5 1.856816 10.45249 7.214238 7.232750e-03 8.334760e-01 Gene746 -1.798331 10.53094 6.846262 8.882693e-03 8.334760e-01 Gene110 1.623148 10.68607 6.737984 9.438120e-03 8.334760e-01 Gene383 1.637140 10.75412 6.687530 9.708965e-03 8.334760e-01 > d1 <- estimateGLMCommonDisp(d, design, verbose=TRUE) Disp = 0.10253 , BCV = 0.3202 > glmFit(d,design,dispersion=dispersion.true, prior.count=0.5/3) An object of class "DGEGLM" $coefficients (Intercept) x Gene1 -7.391745 2.0149958 Gene2 -7.318483 -0.7611895 Gene3 -6.831702 -0.1399478 Gene4 -7.480255 0.5172002 Gene5 -8.747793 1.2870467 995 more rows ... $fitted.values x0 x1 x2 Gene1 2.3570471 18.954454 138.2791328 Gene2 2.5138172 1.089292 0.4282107 Gene3 4.1580452 3.750528 3.0690081 Gene4 2.1012460 3.769592 6.1349937 Gene5 0.5080377 2.136398 8.1502486 995 more rows ... $deviance [1] 6.38037545 1.46644913 1.38532340 0.01593969 1.03894513 995 more elements ... $iter [1] 8 4 4 4 6 995 more elements ... $failed [1] FALSE FALSE FALSE FALSE FALSE 995 more elements ... $method [1] "levenberg" $counts x0 x1 x2 Gene1 0 30 110 Gene2 2 2 0 Gene3 3 6 2 Gene4 2 4 6 Gene5 1 1 9 995 more rows ... $unshrunk.coefficients (Intercept) x Gene1 -7.437763 2.0412762 Gene2 -7.373370 -0.8796273 Gene3 -6.870127 -0.1465014 Gene4 -7.552642 0.5410832 Gene5 -8.972372 1.3929679 995 more rows ... $df.residual [1] 1 1 1 1 1 995 more elements ... $design (Intercept) x 1 1 0 2 1 1 3 1 2 attr(,"assign") [1] 0 1 $offset [,1] [,2] [,3] [1,] 8.295172 8.338525 8.284484 attr(,"class") [1] "CompressedMatrix" attr(,"Dims") [1] 5 3 attr(,"repeat.row") [1] TRUE attr(,"repeat.col") [1] FALSE 995 more rows ... $dispersion [1] 0.1 $prior.count [1] 0.1666667 $samples group lib.size norm.factors x0 1 4001 1.0008730 x1 1 4176 1.0014172 x2 1 3971 0.9977138 $AveLogCPM [1] 13.561832 9.682757 10.447014 10.532113 10.452489 995 more elements ... > > normLibSizes(d$counts,method="TMMwsp") x0 x1 x2 0.9992437 1.0077007 0.9931093 > > d2 <- estimateDisp(d, design) > summary(d2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.05545 0.09511 0.11623 0.11014 0.13329 0.16861 > d2 <- estimateDisp(d, design, prior.df=20) > summary(d2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.04203 0.08586 0.11280 0.11010 0.12369 0.37408 > d2 <- estimateDisp(d, design, robust=TRUE) > summary(d2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.05545 0.09511 0.11623 0.11014 0.13329 0.16861 > > # Exact tests > y <- matrix(rnbinom(20,mu=10,size=3/2),5,4) > group <- factor(c(1,1,2,2)) > ys <- splitIntoGroupsPseudo(y,group,pair=c(1,2)) > exactTestDoubleTail(ys$y1,ys$y2,dispersion=2/3) [1] 0.1334396 0.6343568 0.7280015 0.7124912 0.3919258 > > y <- matrix(rnbinom(5*7,mu=10,size=3/2),5,7) > group <- factor(c(1,1,2,2,3,3,3)) > ys <- splitIntoGroupsPseudo(y,group,pair=c(1,3)) > exactTestDoubleTail(ys$y1,ys$y2,dispersion=2/3) [1] 1.0000000 0.4486382 1.0000000 0.9390317 0.4591241 > exactTestBetaApprox(ys$y1,ys$y2,dispersion=2/3) [1] 1.0000000 0.4492969 1.0000000 0.9421695 0.4589194 > > y[1,3:4] <- 0 > design <- model.matrix(~group) > fit <- glmFit(y,design,dispersion=2/3,prior.count=0.5/7) > summary(fit$coefficients) (Intercept) group2 group3 Min. :-1.817 Min. :-5.0171 Min. :-0.64646 1st Qu.:-1.812 1st Qu.:-1.1565 1st Qu.:-0.13919 Median :-1.712 Median : 0.1994 Median :-0.10441 Mean :-1.625 Mean :-0.9523 Mean :-0.04217 3rd Qu.:-1.429 3rd Qu.: 0.3755 3rd Qu.:-0.04305 Max. :-1.356 Max. : 0.8374 Max. : 0.72227 > > lrt <- glmLRT(fit,contrast=cbind(c(0,1,0),c(0,0,1))) > topTags(lrt) Coefficient: LR test on 2 degrees of freedom logFC.1 logFC.2 logCPM LR PValue FDR 1 -7.2381060 -0.0621100 17.19071 10.7712171 0.004582051 0.02291026 5 -1.6684268 -0.9326507 17.33529 1.7309951 0.420842115 0.90967967 2 1.2080938 1.0420198 18.24544 1.0496688 0.591653347 0.90967967 4 0.5416704 -0.1506381 17.57744 0.3958596 0.820427427 0.90967967 3 0.2876249 -0.2008143 18.06216 0.1893255 0.909679672 0.90967967 > design <- model.matrix(~0+group) > fit <- glmFit(y,design,dispersion=2/3,prior.count=0.5/7) > lrt <- glmLRT(fit,contrast=cbind(c(-1,1,0),c(0,-1,1),c(-1,0,1))) > topTags(lrt) Coefficient: LR test on 2 degrees of freedom logFC.1 logFC.2 logFC.3 logCPM LR PValue FDR 1 -7.2381060 7.1759960 -0.0621100 17.19071 10.7712171 0.004582051 0.02291026 5 -1.6684268 0.7357761 -0.9326507 17.33529 1.7309951 0.420842115 0.90967967 2 1.2080938 -0.1660740 1.0420198 18.24544 1.0496688 0.591653347 0.90967967 4 0.5416704 -0.6923084 -0.1506381 17.57744 0.3958596 0.820427427 0.90967967 3 0.2876249 -0.4884392 -0.2008143 18.06216 0.1893255 0.909679672 0.90967967 > > # simple Good-Turing algorithm runs. > test1 <- 1:9 > freq1 <- c(2018046, 449721, 188933, 105668, 68379, 48190, 35709, 37710, 22280) > goodTuring(rep(test1, freq1)) $P0 [1] 0.3814719 $proportion [1] 8.035111e-08 2.272143e-07 4.060582e-07 5.773690e-07 7.516705e-07 [6] 9.276808e-07 1.104759e-06 1.282549e-06 1.460837e-06 $count [1] 1 2 3 4 5 6 7 8 9 $n [1] 2018046 449721 188933 105668 68379 48190 35709 37710 22280 $n0 [1] 0 > test2 <- c(312, 14491, 16401, 65124, 129797, 323321, 366051, 368599, 405261, 604962) > goodTuring(test2) $P0 [1] 0 $proportion [1] 0.0001362656 0.0063162959 0.0071487846 0.0283850925 0.0565733349 [6] 0.1409223124 0.1595465235 0.1606570896 0.1766365144 0.2636777866 $count [1] 312 14491 16401 65124 129797 323321 366051 368599 405261 604962 $n [1] 1 1 1 1 1 1 1 1 1 1 $n0 [1] 0 > > # Dispersion estimation with fitted values equal to zero > ngenes <- 100 > nsamples <- 3 > y <- matrix(rnbinom(ngenes*nsamples,size=5,mu=10),ngenes,nsamples) > Group <- factor(c(1,2,2)) > design <- model.matrix(~Group) > y[1:5,2:3] <- 0 > y <- DGEList(counts=y,group=Group) > > fit <- glmQLFit(y,design,legacy=FALSE,keep.unit.mat=TRUE) > fit$dispersion [1] 0.1913324 > summary(fit$s2.post) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.3227 0.7307 1.0467 1.1891 1.6050 3.2255 > fit$unit.deviance.adj[1:10,] Sample1 Sample2 Sample3 1 0 0.000000e+00 0.000000e+00 2 0 0.000000e+00 0.000000e+00 3 0 0.000000e+00 0.000000e+00 4 0 0.000000e+00 0.000000e+00 5 0 0.000000e+00 0.000000e+00 6 0 8.141239e-02 9.842350e-02 7 0 3.047603e-01 2.190646e-01 8 0 1.749020e-02 1.620417e-02 9 0 1.098493e+00 6.237134e-01 10 0 7.266233e-05 7.242688e-05 > fit$unit.df.adj[1:10,] Sample1 Sample2 Sample3 1 0 0.0000000 0.0000000 2 0 0.0000000 0.0000000 3 0 0.0000000 0.0000000 4 0 0.0000000 0.0000000 5 0 0.0000000 0.0000000 6 0 0.4865169 0.4880617 7 0 0.4965337 0.4994319 8 0 0.4945979 0.4955795 9 0 0.4905011 0.4917294 10 0 0.4945957 0.4955775 > summary(fit$deviance.adj) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00000 0.08228 0.53816 1.09592 1.72337 5.48746 > summary(fit$df.residual.adj) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.9756 0.9816 0.9356 0.9882 1.0912 > > y <- estimateCommonDisp(y) > y$common.dispersion [1] 0.2407907 > y <- estimateGLMCommonDisp(y,design) > y$common.dispersion [1] 0.2181198 > y <- estimateGLMTrendedDisp(y,design) > y$trended.dispersion[1:10] [1] 0.3398724 0.2889110 0.3398724 0.2769872 0.2494308 0.2562610 0.2368123 [8] 0.2052054 0.2024856 0.1932597 > summary(y$trended.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1814 0.2065 0.2188 0.2276 0.2413 0.3399 > y <- estimateGLMTagwiseDisp(y,design) > y$tagwise.dispersion[1:10] [1] 0.4396666 0.3416568 0.4270057 0.3121297 0.2525866 0.2439067 0.2413469 [8] 0.1702664 0.2125485 0.1349552 > summary(y$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1304 0.1799 0.2212 0.2323 0.2678 0.4397 > y <- estimateDisp(y,design) > y$prior.df [1] 2.18263 > y$common.dispersion [1] 0.2185181 > y$trended.dispersion[1:10] [1] 0.2717585 0.2727238 0.2717585 0.2690969 0.2617797 0.2634692 0.2453099 [8] 0.1931524 0.1930463 0.1928675 > summary(y$trended.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1928 0.1932 0.2086 0.2226 0.2515 0.2728 > y$tagwise.dispersion[1:10] [1] 0.2717585 0.2727238 0.2717585 0.2690969 0.2617797 0.1519257 0.1936500 [8] 0.1015399 0.2475146 0.1013302 > summary(y$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.09719 0.13460 0.19422 0.22321 0.27176 0.61463 > > # glmQLFit > fit <- glmQLFit(y,design,legacy=FALSE,keep.unit.mat=TRUE) > fit$dispersion [1] 0.1929857 > summary(fit$s2.post) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.3222 0.7208 1.0413 1.1832 1.5937 3.2135 > fit$unit.deviance.adj[1:10,] Sample1 Sample2 Sample3 1 0 0.000000e+00 0.000000e+00 2 0 0.000000e+00 0.000000e+00 3 0 0.000000e+00 0.000000e+00 4 0 0.000000e+00 0.000000e+00 5 0 0.000000e+00 0.000000e+00 6 0 8.094957e-02 9.788568e-02 7 0 3.037286e-01 2.182129e-01 8 0 1.737170e-02 1.609300e-02 9 0 1.092091e+00 6.196606e-01 10 0 7.216758e-05 7.193234e-05 > fit$unit.df.adj[1:10,] Sample1 Sample2 Sample3 1 0 0.0000000 0.0000000 2 0 0.0000000 0.0000000 3 0 0.0000000 0.0000000 4 0 0.0000000 0.0000000 5 0 0.0000000 0.0000000 6 0 0.4865290 0.4880672 7 0 0.4968071 0.4997017 8 0 0.4945646 0.4955390 9 0 0.4904706 0.4916914 10 0 0.4945624 0.4955369 > summary(fit$deviance.adj) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.0818 0.5350 1.0900 1.7132 5.4553 > summary(fit$df.residual.adj) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.9756 0.9816 0.9357 0.9882 1.0921 > fit <- glmQLFit(y,design,legacy=TRUE) > summary(fit$dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1928 0.1932 0.2086 0.2226 0.2515 0.2728 > summary(fit$s2.post) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.06246 0.59974 0.79811 0.91038 1.17237 2.64122 > > proc.time() user system elapsed 0.872 0.036 0.904
edgeR.Rcheck/tests/edgeR-Tests.Rout.save
R version 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(edgeR) Loading required package: limma > options(warnPartialMatchArgs=TRUE,warnPartialMatchAttr=TRUE,warnPartialMatchDollar=TRUE) > > set.seed(0); u <- runif(100) > > # generate raw counts from NB, create list object > y <- matrix(rnbinom(80,size=5,mu=10),nrow=20) > y <- rbind(0,c(0,0,2,2),y) > rownames(y) <- paste("Tag",1:nrow(y),sep=".") > d <- DGEList(counts=y,group=rep(1:2,each=2),lib.size=1001:1004) > > filterByExpr(d) Tag.1 Tag.2 Tag.3 Tag.4 Tag.5 Tag.6 Tag.7 Tag.8 Tag.9 Tag.10 Tag.11 FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE Tag.12 Tag.13 Tag.14 Tag.15 Tag.16 Tag.17 Tag.18 Tag.19 Tag.20 Tag.21 Tag.22 TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE > > # estimate common dispersion and find differences in expression > d <- estimateCommonDisp(d) > d$common.dispersion [1] 0.210292 > de <- exactTest(d) > summary(de$table) logFC logCPM PValue Min. :-1.7266 Min. :10.96 Min. :0.01976 1st Qu.:-0.4855 1st Qu.:13.21 1st Qu.:0.33120 Median : 0.2253 Median :13.37 Median :0.56514 Mean : 0.1877 Mean :13.26 Mean :0.54504 3rd Qu.: 0.5258 3rd Qu.:13.70 3rd Qu.:0.81052 Max. : 4.0861 Max. :14.31 Max. :1.00000 > topTags(de) Comparison of groups: 2-1 logFC logCPM PValue FDR Tag.17 2.0450964 13.73726 0.01975954 0.4347099 Tag.21 -1.7265870 13.38327 0.06131012 0.6744114 Tag.6 -1.6329986 12.81479 0.12446044 0.8982100 Tag.2 4.0861092 11.54121 0.16331090 0.8982100 Tag.16 0.9324996 13.57074 0.29050785 0.9655885 Tag.20 0.8543138 13.76364 0.31736609 0.9655885 Tag.12 0.7081170 14.31389 0.37271028 0.9655885 Tag.19 -0.7976602 13.31405 0.40166354 0.9655885 Tag.3 -0.7300410 13.54155 0.42139935 0.9655885 Tag.8 -0.7917906 12.86353 0.47117217 0.9655885 > > d2 <- estimateTagwiseDisp(d,trend="none",prior.df=20) > summary(d2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1757 0.1896 0.1989 0.2063 0.2185 0.2677 > de <- exactTest(d2,dispersion="common") > topTags(de) Comparison of groups: 2-1 logFC logCPM PValue FDR Tag.17 2.0450964 13.73726 0.01975954 0.4347099 Tag.21 -1.7265870 13.38327 0.06131012 0.6744114 Tag.6 -1.6329986 12.81479 0.12446044 0.8982100 Tag.2 4.0861092 11.54121 0.16331090 0.8982100 Tag.16 0.9324996 13.57074 0.29050785 0.9655885 Tag.20 0.8543138 13.76364 0.31736609 0.9655885 Tag.12 0.7081170 14.31389 0.37271028 0.9655885 Tag.19 -0.7976602 13.31405 0.40166354 0.9655885 Tag.3 -0.7300410 13.54155 0.42139935 0.9655885 Tag.8 -0.7917906 12.86353 0.47117217 0.9655885 > > de <- exactTest(d2) > topTags(de) Comparison of groups: 2-1 logFC logCPM PValue FDR Tag.17 2.0450987 13.73726 0.01327001 0.2919403 Tag.21 -1.7265897 13.38327 0.05683886 0.6252275 Tag.6 -1.6329910 12.81479 0.11460208 0.8404152 Tag.2 4.0861092 11.54121 0.16126207 0.8869414 Tag.16 0.9324975 13.57074 0.28103256 0.9669238 Tag.20 0.8543178 13.76364 0.30234789 0.9669238 Tag.12 0.7081149 14.31389 0.37917895 0.9669238 Tag.19 -0.7976633 13.31405 0.40762735 0.9669238 Tag.3 -0.7300478 13.54155 0.40856822 0.9669238 Tag.8 -0.7918243 12.86353 0.49005179 0.9669238 > > d2 <- estimateTagwiseDisp(d,trend="movingave",span=0.4,prior.df=20) > summary(d2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1005 0.1629 0.2064 0.2077 0.2585 0.3164 > de <- exactTest(d2) > topTags(de) Comparison of groups: 2-1 logFC logCPM PValue FDR Tag.17 2.0450951 13.73726 0.02427872 0.5341319 Tag.21 -1.7265927 13.38327 0.05234833 0.5758316 Tag.6 -1.6330014 12.81479 0.12846308 0.8954397 Tag.2 4.0861092 11.54121 0.16280722 0.8954397 Tag.16 0.9324887 13.57074 0.24308201 0.9711975 Tag.20 0.8543044 13.76364 0.35534649 0.9711975 Tag.19 -0.7976535 13.31405 0.38873717 0.9711975 Tag.3 -0.7300525 13.54155 0.40001438 0.9711975 Tag.12 0.7080985 14.31389 0.43530227 0.9711975 Tag.8 -0.7918376 12.86353 0.49782701 0.9711975 > > summary(exactTest(d2,rejection.region="smallp")$table$PValue) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.02428 0.36369 0.55662 0.54319 0.78889 1.00000 > summary(exactTest(d2,rejection.region="deviance")$table$PValue) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.02428 0.36369 0.55662 0.54319 0.78889 1.00000 > > d2 <- estimateTagwiseDisp(d,trend="loess",span=0.8,prior.df=20) > summary(d2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1165 0.1449 0.1832 0.1848 0.2116 0.2825 > de <- exactTest(d2) > topTags(de) Comparison of groups: 2-1 logFC logCPM PValue FDR Tag.17 2.0450979 13.73726 0.01546795 0.3402949 Tag.21 -1.7266049 13.38327 0.03545446 0.3899990 Tag.6 -1.6329841 12.81479 0.10632987 0.7797524 Tag.2 4.0861092 11.54121 0.16057893 0.8831841 Tag.16 0.9324935 13.57074 0.26348818 0.9658389 Tag.20 0.8543140 13.76364 0.31674090 0.9658389 Tag.19 -0.7976354 13.31405 0.35564858 0.9658389 Tag.3 -0.7300593 13.54155 0.38833737 0.9658389 Tag.12 0.7081041 14.31389 0.41513004 0.9658389 Tag.8 -0.7918152 12.86353 0.48483449 0.9658389 > > d2 <- estimateTagwiseDisp(d,trend="tricube",span=0.8,prior.df=20) > summary(d2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1165 0.1449 0.1832 0.1848 0.2116 0.2825 > de <- exactTest(d2) > topTags(de) Comparison of groups: 2-1 logFC logCPM PValue FDR Tag.17 2.0450979 13.73726 0.01546795 0.3402949 Tag.21 -1.7266049 13.38327 0.03545446 0.3899990 Tag.6 -1.6329841 12.81479 0.10632987 0.7797524 Tag.2 4.0861092 11.54121 0.16057893 0.8831841 Tag.16 0.9324935 13.57074 0.26348818 0.9658389 Tag.20 0.8543140 13.76364 0.31674090 0.9658389 Tag.19 -0.7976354 13.31405 0.35564858 0.9658389 Tag.3 -0.7300593 13.54155 0.38833737 0.9658389 Tag.12 0.7081041 14.31389 0.41513004 0.9658389 Tag.8 -0.7918152 12.86353 0.48483449 0.9658389 > > # mglmOneWay > design <- model.matrix(~group,data=d$samples) > mglmOneWay(d[1:10,],design,dispersion=0.2) $coefficients (Intercept) group2 Tag.1 -1.000000e+08 0.000000e+00 Tag.2 -1.000000e+08 1.000000e+08 Tag.3 2.525729e+00 -5.108256e-01 Tag.4 2.525729e+00 1.484200e-01 Tag.5 2.140066e+00 -1.941560e-01 Tag.6 2.079442e+00 -1.163151e+00 Tag.7 2.014903e+00 2.363888e-01 Tag.8 1.945910e+00 -5.596158e-01 Tag.9 1.504077e+00 2.006707e-01 Tag.10 2.302585e+00 2.623643e-01 $fitted.values Sample1 Sample2 Sample3 Sample4 Tag.1 0.0 0.0 0.0 0.0 Tag.2 0.0 0.0 2.0 2.0 Tag.3 12.5 12.5 7.5 7.5 Tag.4 12.5 12.5 14.5 14.5 Tag.5 8.5 8.5 7.0 7.0 Tag.6 8.0 8.0 2.5 2.5 Tag.7 7.5 7.5 9.5 9.5 Tag.8 7.0 7.0 4.0 4.0 Tag.9 4.5 4.5 5.5 5.5 Tag.10 10.0 10.0 13.0 13.0 > mglmOneWay(d[1:10,],design,dispersion=0) $coefficients (Intercept) group2 Tag.1 -1.000000e+08 0.000000e+00 Tag.2 -1.000000e+08 1.000000e+08 Tag.3 2.525729e+00 -5.108256e-01 Tag.4 2.525729e+00 1.484200e-01 Tag.5 2.140066e+00 -1.941560e-01 Tag.6 2.079442e+00 -1.163151e+00 Tag.7 2.014903e+00 2.363888e-01 Tag.8 1.945910e+00 -5.596158e-01 Tag.9 1.504077e+00 2.006707e-01 Tag.10 2.302585e+00 2.623643e-01 $fitted.values Sample1 Sample2 Sample3 Sample4 Tag.1 0.0 0.0 0.0 0.0 Tag.2 0.0 0.0 2.0 2.0 Tag.3 12.5 12.5 7.5 7.5 Tag.4 12.5 12.5 14.5 14.5 Tag.5 8.5 8.5 7.0 7.0 Tag.6 8.0 8.0 2.5 2.5 Tag.7 7.5 7.5 9.5 9.5 Tag.8 7.0 7.0 4.0 4.0 Tag.9 4.5 4.5 5.5 5.5 Tag.10 10.0 10.0 13.0 13.0 > > fit <- glmFit(d,design,dispersion=d$common.dispersion,prior.count=0.5/4) > lrt <- glmLRT(fit,coef=2) > topTags(lrt) Coefficient: group2 logFC logCPM LR PValue FDR Tag.17 2.0450964 13.73726 6.0485417 0.01391779 0.3058698 Tag.2 4.0861092 11.54121 4.8400340 0.02780635 0.3058698 Tag.21 -1.7265870 13.38327 4.0777825 0.04345065 0.3186381 Tag.6 -1.6329986 12.81479 3.0078205 0.08286364 0.4557500 Tag.16 0.9324996 13.57074 1.3477682 0.24566867 0.8276702 Tag.20 0.8543138 13.76364 1.1890032 0.27553071 0.8276702 Tag.19 -0.7976602 13.31405 0.9279151 0.33540526 0.8276702 Tag.12 0.7081170 14.31389 0.9095513 0.34023349 0.8276702 Tag.3 -0.7300410 13.54155 0.8300307 0.36226364 0.8276702 Tag.8 -0.7917906 12.86353 0.7830377 0.37621371 0.8276702 > > fit <- glmFit(d,design,dispersion=d$common.dispersion,prior.count=0.5) > summary(fit$coefficients) (Intercept) group2 Min. :-7.604 Min. :-1.13681 1st Qu.:-4.895 1st Qu.:-0.32341 Median :-4.713 Median : 0.15083 Mean :-4.940 Mean : 0.07817 3rd Qu.:-4.524 3rd Qu.: 0.35163 Max. :-4.107 Max. : 1.60864 > > fit <- glmFit(d,design,prior.count=0.5/4) > lrt <- glmLRT(fit,coef=2) > topTags(lrt) Coefficient: group2 logFC logCPM LR PValue FDR Tag.17 2.0450964 13.73726 6.0485417 0.01391779 0.3058698 Tag.2 4.0861092 11.54121 4.8400340 0.02780635 0.3058698 Tag.21 -1.7265870 13.38327 4.0777825 0.04345065 0.3186381 Tag.6 -1.6329986 12.81479 3.0078205 0.08286364 0.4557500 Tag.16 0.9324996 13.57074 1.3477682 0.24566867 0.8276702 Tag.20 0.8543138 13.76364 1.1890032 0.27553071 0.8276702 Tag.19 -0.7976602 13.31405 0.9279151 0.33540526 0.8276702 Tag.12 0.7081170 14.31389 0.9095513 0.34023349 0.8276702 Tag.3 -0.7300410 13.54155 0.8300307 0.36226364 0.8276702 Tag.8 -0.7917906 12.86353 0.7830377 0.37621371 0.8276702 > > dglm <- estimateGLMCommonDisp(d,design) > dglm$common.dispersion [1] 0.2033282 > dglm <- estimateGLMTagwiseDisp(dglm,design,prior.df=20) > summary(dglm$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1756 0.1879 0.1998 0.2031 0.2135 0.2578 > fit <- glmFit(dglm,design,prior.count=0.5/4) > lrt <- glmLRT(fit,coef=2) > topTags(lrt) Coefficient: group2 logFC logCPM LR PValue FDR Tag.17 2.0450988 13.73727 6.8001118 0.009115216 0.2005348 Tag.2 4.0861092 11.54122 4.8594088 0.027495756 0.2872068 Tag.21 -1.7265904 13.38327 4.2537154 0.039164558 0.2872068 Tag.6 -1.6329904 12.81479 3.1763761 0.074710253 0.4109064 Tag.16 0.9324970 13.57074 1.4126709 0.234613512 0.8499599 Tag.20 0.8543183 13.76364 1.2721097 0.259371274 0.8499599 Tag.19 -0.7976614 13.31405 0.9190392 0.337727381 0.8499599 Tag.12 0.7081163 14.31389 0.9014515 0.342392806 0.8499599 Tag.3 -0.7300488 13.54155 0.8817937 0.347710872 0.8499599 Tag.8 -0.7918166 12.86353 0.7356185 0.391068049 0.8603497 > dglm <- estimateGLMTrendedDisp(dglm,design) > summary(dglm$trended.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1522 0.1676 0.1740 0.1887 0.2000 0.3469 > dglm <- estimateGLMTrendedDisp(dglm,design,method="power") > summary(dglm$trended.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1522 0.1676 0.1740 0.1887 0.2000 0.3469 > dglm <- estimateGLMTrendedDisp(dglm,design,method="spline") > summary(dglm$trended.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.09353 0.11082 0.15463 0.19006 0.23050 0.52006 > dglm <- estimateGLMTrendedDisp(dglm,design,method="bin.spline") > summary(dglm$trended.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1997 0.1997 0.1997 0.1997 0.1997 0.1997 > dglm <- estimateGLMTagwiseDisp(dglm,design,prior.df=20) > summary(dglm$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1385 0.1792 0.1964 0.1935 0.2026 0.2709 > > dglm2 <- estimateDisp(dglm, design) > summary(dglm2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1652 0.1740 0.1821 0.1852 0.1909 0.2259 > dglm2 <- estimateDisp(dglm, design, prior.df=20) > summary(dglm2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1527 0.1669 0.1814 0.1858 0.1951 0.2497 > dglm2 <- estimateDisp(dglm, design, robust=TRUE) > summary(dglm2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1652 0.1735 0.1822 0.1854 0.1905 0.2280 > > # Continuous trend > nlibs <- 3 > ntags <- 1000 > dispersion.true <- 0.1 > # Make first transcript respond to covariate x > x <- 0:2 > design <- model.matrix(~x) > beta.true <- cbind(Beta1=2,Beta2=c(2,rep(0,ntags-1))) > mu.true <- 2^(beta.true %*% t(design)) > # Generate count data > y <- rnbinom(ntags*nlibs,mu=mu.true,size=1/dispersion.true) > y <- matrix(y,ntags,nlibs) > colnames(y) <- c("x0","x1","x2") > rownames(y) <- paste("Gene",1:ntags,sep="") > d <- DGEList(y) > d <- normLibSizes(d,method="TMM") > fit <- glmFit(d, design, dispersion=dispersion.true, prior.count=0.5/3) > results <- glmLRT(fit, coef=2) > topTags(results) Coefficient: x logFC logCPM LR PValue FDR Gene1 2.907024 13.56183 38.738512 4.845536e-10 4.845536e-07 Gene61 2.855317 10.27136 10.738307 1.049403e-03 5.247015e-01 Gene62 -2.123902 10.53174 8.818703 2.981585e-03 8.334760e-01 Gene134 -1.949073 10.53355 8.125889 4.363759e-03 8.334760e-01 Gene740 -1.610046 10.94907 8.013408 4.643227e-03 8.334760e-01 Gene354 2.022698 10.45066 7.826308 5.149118e-03 8.334760e-01 Gene5 1.856816 10.45249 7.214238 7.232750e-03 8.334760e-01 Gene746 -1.798331 10.53094 6.846262 8.882693e-03 8.334760e-01 Gene110 1.623148 10.68607 6.737984 9.438120e-03 8.334760e-01 Gene383 1.637140 10.75412 6.687530 9.708965e-03 8.334760e-01 > d1 <- estimateGLMCommonDisp(d, design, verbose=TRUE) Disp = 0.10253 , BCV = 0.3202 > glmFit(d,design,dispersion=dispersion.true, prior.count=0.5/3) An object of class "DGEGLM" $coefficients (Intercept) x Gene1 -7.391745 2.0149958 Gene2 -7.318483 -0.7611895 Gene3 -6.831702 -0.1399478 Gene4 -7.480255 0.5172002 Gene5 -8.747793 1.2870467 995 more rows ... $fitted.values x0 x1 x2 Gene1 2.3570471 18.954454 138.2791328 Gene2 2.5138172 1.089292 0.4282107 Gene3 4.1580452 3.750528 3.0690081 Gene4 2.1012460 3.769592 6.1349937 Gene5 0.5080377 2.136398 8.1502486 995 more rows ... $deviance [1] 6.38037545 1.46644913 1.38532340 0.01593969 1.03894513 995 more elements ... $iter [1] 8 4 4 4 6 995 more elements ... $failed [1] FALSE FALSE FALSE FALSE FALSE 995 more elements ... $method [1] "levenberg" $counts x0 x1 x2 Gene1 0 30 110 Gene2 2 2 0 Gene3 3 6 2 Gene4 2 4 6 Gene5 1 1 9 995 more rows ... $unshrunk.coefficients (Intercept) x Gene1 -7.437763 2.0412762 Gene2 -7.373370 -0.8796273 Gene3 -6.870127 -0.1465014 Gene4 -7.552642 0.5410832 Gene5 -8.972372 1.3929679 995 more rows ... $df.residual [1] 1 1 1 1 1 995 more elements ... $design (Intercept) x 1 1 0 2 1 1 3 1 2 attr(,"assign") [1] 0 1 $offset [,1] [,2] [,3] [1,] 8.295172 8.338525 8.284484 attr(,"class") [1] "CompressedMatrix" attr(,"Dims") [1] 5 3 attr(,"repeat.row") [1] TRUE attr(,"repeat.col") [1] FALSE 995 more rows ... $dispersion [1] 0.1 $prior.count [1] 0.1666667 $samples group lib.size norm.factors x0 1 4001 1.0008730 x1 1 4176 1.0014172 x2 1 3971 0.9977138 $AveLogCPM [1] 13.561832 9.682757 10.447014 10.532113 10.452489 995 more elements ... > > normLibSizes(d$counts,method="TMMwsp") x0 x1 x2 0.9992437 1.0077007 0.9931093 > > d2 <- estimateDisp(d, design) > summary(d2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.05545 0.09511 0.11623 0.11014 0.13329 0.16861 > d2 <- estimateDisp(d, design, prior.df=20) > summary(d2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.04203 0.08586 0.11280 0.11010 0.12369 0.37408 > d2 <- estimateDisp(d, design, robust=TRUE) > summary(d2$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.05545 0.09511 0.11623 0.11014 0.13329 0.16861 > > # Exact tests > y <- matrix(rnbinom(20,mu=10,size=3/2),5,4) > group <- factor(c(1,1,2,2)) > ys <- splitIntoGroupsPseudo(y,group,pair=c(1,2)) > exactTestDoubleTail(ys$y1,ys$y2,dispersion=2/3) [1] 0.1334396 0.6343568 0.7280015 0.7124912 0.3919258 > > y <- matrix(rnbinom(5*7,mu=10,size=3/2),5,7) > group <- factor(c(1,1,2,2,3,3,3)) > ys <- splitIntoGroupsPseudo(y,group,pair=c(1,3)) > exactTestDoubleTail(ys$y1,ys$y2,dispersion=2/3) [1] 1.0000000 0.4486382 1.0000000 0.9390317 0.4591241 > exactTestBetaApprox(ys$y1,ys$y2,dispersion=2/3) [1] 1.0000000 0.4492969 1.0000000 0.9421695 0.4589194 > > y[1,3:4] <- 0 > design <- model.matrix(~group) > fit <- glmFit(y,design,dispersion=2/3,prior.count=0.5/7) > summary(fit$coefficients) (Intercept) group2 group3 Min. :-1.817 Min. :-5.0171 Min. :-0.64646 1st Qu.:-1.812 1st Qu.:-1.1565 1st Qu.:-0.13919 Median :-1.712 Median : 0.1994 Median :-0.10441 Mean :-1.625 Mean :-0.9523 Mean :-0.04217 3rd Qu.:-1.429 3rd Qu.: 0.3755 3rd Qu.:-0.04305 Max. :-1.356 Max. : 0.8374 Max. : 0.72227 > > lrt <- glmLRT(fit,contrast=cbind(c(0,1,0),c(0,0,1))) > topTags(lrt) Coefficient: LR test on 2 degrees of freedom logFC.1 logFC.2 logCPM LR PValue FDR 1 -7.2381060 -0.0621100 17.19071 10.7712171 0.004582051 0.02291026 5 -1.6684268 -0.9326507 17.33529 1.7309951 0.420842115 0.90967967 2 1.2080938 1.0420198 18.24544 1.0496688 0.591653347 0.90967967 4 0.5416704 -0.1506381 17.57744 0.3958596 0.820427427 0.90967967 3 0.2876249 -0.2008143 18.06216 0.1893255 0.909679672 0.90967967 > design <- model.matrix(~0+group) > fit <- glmFit(y,design,dispersion=2/3,prior.count=0.5/7) > lrt <- glmLRT(fit,contrast=cbind(c(-1,1,0),c(0,-1,1),c(-1,0,1))) > topTags(lrt) Coefficient: LR test on 2 degrees of freedom logFC.1 logFC.2 logFC.3 logCPM LR PValue FDR 1 -7.2381060 7.1759960 -0.0621100 17.19071 10.7712171 0.004582051 0.02291026 5 -1.6684268 0.7357761 -0.9326507 17.33529 1.7309951 0.420842115 0.90967967 2 1.2080938 -0.1660740 1.0420198 18.24544 1.0496688 0.591653347 0.90967967 4 0.5416704 -0.6923084 -0.1506381 17.57744 0.3958596 0.820427427 0.90967967 3 0.2876249 -0.4884392 -0.2008143 18.06216 0.1893255 0.909679672 0.90967967 > > # simple Good-Turing algorithm runs. > test1 <- 1:9 > freq1 <- c(2018046, 449721, 188933, 105668, 68379, 48190, 35709, 37710, 22280) > goodTuring(rep(test1, freq1)) $P0 [1] 0.3814719 $proportion [1] 8.035111e-08 2.272143e-07 4.060582e-07 5.773690e-07 7.516705e-07 [6] 9.276808e-07 1.104759e-06 1.282549e-06 1.460837e-06 $count [1] 1 2 3 4 5 6 7 8 9 $n [1] 2018046 449721 188933 105668 68379 48190 35709 37710 22280 $n0 [1] 0 > test2 <- c(312, 14491, 16401, 65124, 129797, 323321, 366051, 368599, 405261, 604962) > goodTuring(test2) $P0 [1] 0 $proportion [1] 0.0001362656 0.0063162959 0.0071487846 0.0283850925 0.0565733349 [6] 0.1409223124 0.1595465235 0.1606570896 0.1766365144 0.2636777866 $count [1] 312 14491 16401 65124 129797 323321 366051 368599 405261 604962 $n [1] 1 1 1 1 1 1 1 1 1 1 $n0 [1] 0 > > # Dispersion estimation with fitted values equal to zero > ngenes <- 100 > nsamples <- 3 > y <- matrix(rnbinom(ngenes*nsamples,size=5,mu=10),ngenes,nsamples) > Group <- factor(c(1,2,2)) > design <- model.matrix(~Group) > y[1:5,2:3] <- 0 > y <- DGEList(counts=y,group=Group) > > fit <- glmQLFit(y,design,legacy=FALSE,keep.unit.mat=TRUE) > fit$dispersion [1] 0.1913324 > summary(fit$s2.post) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.3227 0.7307 1.0467 1.1891 1.6050 3.2255 > fit$unit.deviance.adj[1:10,] Sample1 Sample2 Sample3 1 0 0.000000e+00 0.000000e+00 2 0 0.000000e+00 0.000000e+00 3 0 0.000000e+00 0.000000e+00 4 0 0.000000e+00 0.000000e+00 5 0 0.000000e+00 0.000000e+00 6 0 8.141239e-02 9.842350e-02 7 0 3.047603e-01 2.190646e-01 8 0 1.749020e-02 1.620417e-02 9 0 1.098493e+00 6.237134e-01 10 0 7.266233e-05 7.242688e-05 > fit$unit.df.adj[1:10,] Sample1 Sample2 Sample3 1 0 0.0000000 0.0000000 2 0 0.0000000 0.0000000 3 0 0.0000000 0.0000000 4 0 0.0000000 0.0000000 5 0 0.0000000 0.0000000 6 0 0.4865169 0.4880617 7 0 0.4965337 0.4994319 8 0 0.4945979 0.4955795 9 0 0.4905011 0.4917294 10 0 0.4945957 0.4955775 > summary(fit$deviance.adj) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00000 0.08228 0.53816 1.09592 1.72337 5.48746 > summary(fit$df.residual.adj) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.9756 0.9816 0.9356 0.9882 1.0912 > > y <- estimateCommonDisp(y) > y$common.dispersion [1] 0.2407907 > y <- estimateGLMCommonDisp(y,design) > y$common.dispersion [1] 0.2181198 > y <- estimateGLMTrendedDisp(y,design) > y$trended.dispersion[1:10] [1] 0.3398724 0.2889110 0.3398724 0.2769872 0.2494308 0.2562610 0.2368123 [8] 0.2052054 0.2024856 0.1932597 > summary(y$trended.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1814 0.2065 0.2188 0.2276 0.2413 0.3399 > y <- estimateGLMTagwiseDisp(y,design) > y$tagwise.dispersion[1:10] [1] 0.4396666 0.3416568 0.4270057 0.3121297 0.2525866 0.2439067 0.2413469 [8] 0.1702664 0.2125485 0.1349552 > summary(y$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1304 0.1799 0.2212 0.2323 0.2678 0.4397 > y <- estimateDisp(y,design) > y$prior.df [1] 2.18263 > y$common.dispersion [1] 0.2185181 > y$trended.dispersion[1:10] [1] 0.2717585 0.2727238 0.2717585 0.2690969 0.2617797 0.2634692 0.2453099 [8] 0.1931524 0.1930463 0.1928675 > summary(y$trended.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1928 0.1932 0.2086 0.2226 0.2515 0.2728 > y$tagwise.dispersion[1:10] [1] 0.2717585 0.2727238 0.2717585 0.2690969 0.2617797 0.1519257 0.1936500 [8] 0.1015399 0.2475146 0.1013302 > summary(y$tagwise.dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.09719 0.13460 0.19422 0.22321 0.27176 0.61463 > > # glmQLFit > fit <- glmQLFit(y,design,legacy=FALSE,keep.unit.mat=TRUE) > fit$dispersion [1] 0.1929857 > summary(fit$s2.post) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.3222 0.7208 1.0413 1.1832 1.5937 3.2135 > fit$unit.deviance.adj[1:10,] Sample1 Sample2 Sample3 1 0 0.000000e+00 0.000000e+00 2 0 0.000000e+00 0.000000e+00 3 0 0.000000e+00 0.000000e+00 4 0 0.000000e+00 0.000000e+00 5 0 0.000000e+00 0.000000e+00 6 0 8.094957e-02 9.788568e-02 7 0 3.037286e-01 2.182129e-01 8 0 1.737170e-02 1.609300e-02 9 0 1.092091e+00 6.196606e-01 10 0 7.216758e-05 7.193234e-05 > fit$unit.df.adj[1:10,] Sample1 Sample2 Sample3 1 0 0.0000000 0.0000000 2 0 0.0000000 0.0000000 3 0 0.0000000 0.0000000 4 0 0.0000000 0.0000000 5 0 0.0000000 0.0000000 6 0 0.4865290 0.4880672 7 0 0.4968071 0.4997017 8 0 0.4945646 0.4955390 9 0 0.4904706 0.4916914 10 0 0.4945624 0.4955369 > summary(fit$deviance.adj) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.0818 0.5350 1.0900 1.7132 5.4553 > summary(fit$df.residual.adj) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.9756 0.9816 0.9357 0.9882 1.0921 > fit <- glmQLFit(y,design,legacy=TRUE) > summary(fit$dispersion) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1928 0.1932 0.2086 0.2226 0.2515 0.2728 > summary(fit$s2.post) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.06246 0.59974 0.79811 0.91038 1.17237 2.64122 > > proc.time() user system elapsed 0.17 0.03 2.01
edgeR.Rcheck/edgeR-Ex.timings
name | user | system | elapsed | |
DGEList | 0.005 | 0.001 | 0.006 | |
SE2DGEList | 0 | 0 | 0 | |
Seurat2PB | 0 | 0 | 0 | |
WLEB | 0.008 | 0.000 | 0.008 | |
addPriorCount | 0.001 | 0.000 | 0.001 | |
adjustedProfileLik | 0.001 | 0.001 | 0.002 | |
aveLogCPM | 0.000 | 0.000 | 0.001 | |
binomTest | 0.001 | 0.000 | 0.001 | |
calcNormFactors | 0.002 | 0.000 | 0.003 | |
camera | 0.065 | 0.002 | 0.066 | |
catchSalmon | 0 | 0 | 0 | |
cbind | 0 | 0 | 0 | |
commonCondLogLikDerDelta | 0.000 | 0.000 | 0.001 | |
condLogLikDerSize | 0.001 | 0.000 | 0.000 | |
cpm | 0.001 | 0.001 | 0.001 | |
cutWithMinN | 0 | 0 | 0 | |
decidetestsDGE | 0.005 | 0.000 | 0.004 | |
dglmStdResid | 0.004 | 0.001 | 0.004 | |
diffSpliceDGE | 0.012 | 0.002 | 0.014 | |
dim | 0 | 0 | 0 | |
dispBinTrend | 0.075 | 0.002 | 0.078 | |
dispCoxReid | 0.004 | 0.000 | 0.004 | |
dispCoxReidInterpolateTagwise | 0.004 | 0.001 | 0.006 | |
dispCoxReidSplineTrend | 0.109 | 0.001 | 0.110 | |
dropEmptyLevels | 0.001 | 0.000 | 0.000 | |
edgeRUsersGuide | 0.000 | 0.000 | 0.001 | |
effectiveLibSizes | 0.002 | 0.000 | 0.001 | |
equalizeLibSizes | 0.004 | 0.000 | 0.005 | |
estimateCommonDisp | 0.007 | 0.000 | 0.007 | |
estimateDisp | 0.047 | 0.002 | 0.049 | |
estimateExonGenewisedisp | 0.004 | 0.000 | 0.003 | |
estimateGLMCommonDisp | 0.011 | 0.000 | 0.012 | |
estimateGLMRobustDisp | 0.090 | 0.002 | 0.092 | |
estimateGLMTagwiseDisp | 0.023 | 0.000 | 0.024 | |
estimateGLMTrendedDisp | 0.019 | 0.000 | 0.020 | |
estimateTagwiseDisp | 0.009 | 0.000 | 0.009 | |
estimateTrendedDisp | 0.060 | 0.003 | 0.063 | |
exactTest | 0.007 | 0.001 | 0.008 | |
expandAsMatrix | 0 | 0 | 0 | |
filterByExpr | 0 | 0 | 0 | |
getCounts | 0.001 | 0.000 | 0.002 | |
getPriorN | 0.001 | 0.000 | 0.001 | |
gini | 0 | 0 | 0 | |
glmLRT | 0 | 0 | 0 | |
glmQLFTest | 0 | 0 | 0 | |
glmQLFit | 0.102 | 0.002 | 0.103 | |
glmTreat | 0.004 | 0.000 | 0.005 | |
glmfit | 0.002 | 0.000 | 0.003 | |
goana | 0.001 | 0.000 | 0.000 | |
gof | 0.003 | 0.001 | 0.005 | |
goodTuring | 0.002 | 0.001 | 0.002 | |
head | 0.001 | 0.000 | 0.001 | |
loessByCol | 0.000 | 0.000 | 0.001 | |
maPlot | 0.011 | 0.001 | 0.012 | |
makeCompressedMatrix | 0.001 | 0.001 | 0.002 | |
maximizeInterpolant | 0 | 0 | 0 | |
maximizeQuadratic | 0 | 0 | 0 | |
meanvar | 0.034 | 0.002 | 0.034 | |
mglm | 0.001 | 0.000 | 0.001 | |
modelMatrixMeth | 0.001 | 0.001 | 0.001 | |
movingAverageByCol | 0 | 0 | 0 | |
nbinomDeviance | 0 | 0 | 0 | |
nbinomUnitDeviance | 0 | 0 | 0 | |
nearestReftoX | 0 | 0 | 0 | |
nearestTSS | 1.770 | 0.053 | 1.822 | |
normalizeBetweenArraysDGEList | 0.003 | 0.000 | 0.004 | |
plotBCV | 0.094 | 0.004 | 0.098 | |
plotExonUsage | 0.012 | 0.000 | 0.013 | |
plotMDS.DGEList | 0.006 | 0.001 | 0.006 | |
plotQLDisp | 0.127 | 0.006 | 0.133 | |
plotSmear | 0.170 | 0.009 | 0.179 | |
predFC | 0.037 | 0.001 | 0.038 | |
q2qnbinom | 0 | 0 | 0 | |
read10X | 0 | 0 | 0 | |
readDGE | 0 | 0 | 0 | |
roast.DGEGLM | 0.015 | 0.001 | 0.017 | |
roast.DGEList | 0.026 | 0.001 | 0.027 | |
romer.DGEGLM | 1.072 | 0.052 | 1.123 | |
romer.DGEList | 1.083 | 0.050 | 1.132 | |
rowsum | 0.002 | 0.000 | 0.002 | |
scaleOffset | 0 | 0 | 0 | |
spliceVariants | 0.003 | 0.000 | 0.004 | |
splitIntoGroups | 0.001 | 0.000 | 0.000 | |
subsetting | 0.003 | 0.001 | 0.003 | |
sumTechReps | 0 | 0 | 0 | |
systematicSubset | 0.001 | 0.000 | 0.000 | |
thinCounts | 0 | 0 | 0 | |
topTags | 0.004 | 0.000 | 0.004 | |
validDGEList | 0.000 | 0.001 | 0.000 | |
weightedCondLogLikDerDelta | 0.001 | 0.000 | 0.001 | |
zscoreNBinom | 0 | 0 | 0 | |