Back to Multiple platform build/check report for BioC 3.13 |
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This page was generated on 2021-10-15 15:06:03 -0400 (Fri, 15 Oct 2021).
To the developers/maintainers of the edgeR package: - Please 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 How and When does the builder pull? When will my changes propagate? here for more information. - Make sure to use the following settings in order to reproduce any error or warning you see on this page. |
Package 557/2041 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
edgeR 3.34.1 (landing page) Yunshun Chen
| nebbiolo1 | Linux (Ubuntu 20.04.2 LTS) / x86_64 | OK | OK | OK | |||||||||
tokay2 | Windows Server 2012 R2 Standard / x64 | OK | OK | OK | OK | |||||||||
machv2 | macOS 10.14.6 Mojave / x86_64 | OK | OK | OK | OK | |||||||||
Package: edgeR |
Version: 3.34.1 |
Command: C:\Users\biocbuild\bbs-3.13-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:edgeR.install-out.txt --library=C:\Users\biocbuild\bbs-3.13-bioc\R\library --no-vignettes --timings edgeR_3.34.1.tar.gz |
StartedAt: 2021-10-14 22:40:05 -0400 (Thu, 14 Oct 2021) |
EndedAt: 2021-10-14 22:42:42 -0400 (Thu, 14 Oct 2021) |
EllapsedTime: 157.3 seconds |
RetCode: 0 |
Status: OK |
CheckDir: edgeR.Rcheck |
Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### C:\Users\biocbuild\bbs-3.13-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:edgeR.install-out.txt --library=C:\Users\biocbuild\bbs-3.13-bioc\R\library --no-vignettes --timings edgeR_3.34.1.tar.gz ### ############################################################################## ############################################################################## * using log directory 'C:/Users/biocbuild/bbs-3.13-bioc/meat/edgeR.Rcheck' * using R version 4.1.1 (2021-08-10) * using platform: x86_64-w64-mingw32 (64-bit) * using session charset: ISO8859-1 * using option '--no-vignettes' * checking for file 'edgeR/DESCRIPTION' ... OK * this is package 'edgeR' version '3.34.1' * 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 'edgeR' can be installed ... OK * checking installed package size ... NOTE installed size is 6.2Mb sub-directories of 1Mb or more: doc 1.6Mb libs 2.4Mb * checking package directory ... OK * checking 'build' directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking R files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * loading checks for arch 'i386' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK * loading checks for arch 'x64' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK * checking 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 for i386 is not available Note: information on .o files for x64 is not available File 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/edgeR/libs/i386/edgeR.dll': Found 'abort', possibly from 'abort' (C), 'runtime' (Fortran) Found 'exit', possibly from 'exit' (C), 'stop' (Fortran) Found 'printf', possibly from 'printf' (C) File 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/edgeR/libs/x64/edgeR.dll': Found 'abort', possibly from 'abort' (C), 'runtime' (Fortran) Found 'exit', possibly from 'exit' (C), 'stop' (Fortran) Found 'printf', possibly from 'printf' (C) Compiled code should not call entry points which might terminate R nor write to stdout/stderr instead of to the console, nor use Fortran I/O nor system RNGs. The detected symbols are linked into the code but might come from libraries and not actually be called. See 'Writing portable packages' in the 'Writing R Extensions' manual. * checking installed files from 'inst/doc' ... OK * checking files in 'vignettes' ... OK * checking examples ... ** running examples for arch 'i386' ... OK Examples with CPU (user + system) or elapsed time > 5s user system elapsed nearestTSS 4.69 0.22 5.02 ** running examples for arch 'x64' ... OK * checking for unstated dependencies in 'tests' ... OK * checking tests ... ** running tests for arch 'i386' ... Running 'edgeR-Tests.R' Comparing 'edgeR-Tests.Rout' to 'edgeR-Tests.Rout.save' ... OK OK ** running tests for arch 'x64' ... 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 in 'inst/doc' ... OK * checking running R code from vignettes ... SKIPPED * checking re-building of vignette outputs ... SKIPPED * checking PDF version of manual ... OK * DONE Status: 2 NOTEs See 'C:/Users/biocbuild/bbs-3.13-bioc/meat/edgeR.Rcheck/00check.log' for details.
edgeR.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### C:\cygwin\bin\curl.exe -O http://155.52.207.165/BBS/3.13/bioc/src/contrib/edgeR_3.34.1.tar.gz && rm -rf edgeR.buildbin-libdir && mkdir edgeR.buildbin-libdir && C:\Users\biocbuild\bbs-3.13-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=edgeR.buildbin-libdir edgeR_3.34.1.tar.gz && C:\Users\biocbuild\bbs-3.13-bioc\R\bin\R.exe CMD INSTALL edgeR_3.34.1.zip && rm edgeR_3.34.1.tar.gz edgeR_3.34.1.zip ### ############################################################################## ############################################################################## % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0 100 1542k 100 1542k 0 0 3471k 0 --:--:-- --:--:-- --:--:-- 3480k install for i386 * installing *source* package 'edgeR' ... ** using staged installation ** libs "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_add_prior_count.cpp -o R_add_prior_count.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_ave_log_cpm.cpp -o R_ave_log_cpm.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_calculate_cpm.cpp -o R_calculate_cpm.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_check_poisson_bound.cpp -o R_check_poisson_bound.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_compute_apl.cpp -o R_compute_apl.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_compute_nbdev.cpp -o R_compute_nbdev.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_exact_test_by_deviance.cpp -o R_exact_test_by_deviance.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_fit_levenberg.cpp -o R_fit_levenberg.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_fit_one_group.cpp -o R_fit_one_group.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_get_one_way_fitted.cpp -o R_get_one_way_fitted.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_initialize_levenberg.cpp -o R_initialize_levenberg.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_loess_by_col.cpp -o R_loess_by_col.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_maximize_interpolant.cpp -o R_maximize_interpolant.o "C:/rtools40/mingw32/bin/"gcc -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -std=gnu99 -mfpmath=sse -msse2 -mstackrealign -c R_process_hairpin_reads.c -o R_process_hairpin_reads.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_simple_good_turing.cpp -o R_simple_good_turing.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c add_prior.cpp -o add_prior.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c adj_coxreid.cpp -o adj_coxreid.o "C:/rtools40/mingw32/bin/"gcc -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -std=gnu99 -mfpmath=sse -msse2 -mstackrealign -c fmm_spline.c -o fmm_spline.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c glm_levenberg.cpp -o glm_levenberg.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c glm_one_group.cpp -o glm_one_group.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c init.cpp -o init.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c interpolator.cpp -o interpolator.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c nbdev.cpp -o nbdev.o "C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"c:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c objects.cpp -o objects.o C:/rtools40/mingw32/bin/g++ -shared -s -static-libgcc -o edgeR.dll tmp.def R_add_prior_count.o R_ave_log_cpm.o R_calculate_cpm.o R_check_poisson_bound.o R_compute_apl.o R_compute_nbdev.o R_exact_test_by_deviance.o R_fit_levenberg.o R_fit_one_group.o R_get_one_way_fitted.o R_initialize_levenberg.o R_loess_by_col.o R_maximize_interpolant.o R_process_hairpin_reads.o R_simple_good_turing.o add_prior.o adj_coxreid.o fmm_spline.o glm_levenberg.o glm_one_group.o init.o interpolator.o nbdev.o objects.o -LC:/Users/BIOCBU~1/BBS-3~1.13-/R/bin/i386 -lRlapack -LC:/Users/BIOCBU~1/BBS-3~1.13-/R/bin/i386 -lRblas -lgfortran -lm -lquadmath -Lc:/extsoft/lib/i386 -Lc:/extsoft/lib -LC:/Users/BIOCBU~1/BBS-3~1.13-/R/bin/i386 -lR installing to C:/Users/biocbuild/bbs-3.13-bioc/meat/edgeR.buildbin-libdir/00LOCK-edgeR/00new/edgeR/libs/i386 ** R ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices converting help for package 'edgeR' finding HTML links ... done DGEExact-class html DGEGLM-class html DGELRT-class html DGEList-class html DGEList html SE2DGEList html WLEB html addPriorCount html adjustedProfileLik html asdataframe html asmatrix html aveLogCPM html binomTest html calcNormFactors html camera.DGEList html finding level-2 HTML links ... done catchSalmon html cbind html commonCondLogLikDerDelta html condLogLikDerSize html cpm html cutWithMinN html decidetestsDGE html dglmStdResid html diffSpliceDGE html dim html dimnames html dispBinTrend html dispCoxReid html dispCoxReidInterpolateTagwise html dispCoxReidSplineTrend html dropEmptyLevels html edgeR-package html edgeRUsersGuide html effectiveLibSizes html equalizeLibSizes html estimateCommonDisp html estimateDisp html estimateExonGenewisedisp html estimateGLMCommonDisp html estimateGLMRobustDisp html estimateGLMTagwiseDisp html estimateGLMTrendedDisp html estimateTagwiseDisp html estimateTrendedDisp html exactTest html expandAsMatrix html featureCounts2DGEList html filterByExpr html getCounts html getPriorN html gini html glmQLFTest html glmTreat html glmfit html goana html gof html goodTuring html head html loessByCol html maPlot html makeCompressedMatrix html maximizeInterpolant html maximizeQuadratic html meanvar html mglm html modelMatrixMeth html movingAverageByCol html nbinomDeviance html nbinomUnitDeviance html nearestReftoX html nearestTSS html normalizeChIPtoInput html plotBCV html plotExonUsage html plotMD html plotMDS.DGEList html plotQLDisp html plotSmear html plotSpliceDGE html predFC html processAmplicons html q2qnbinom html read10X html readBismark2DGE html readDGE html roast.DGEList html romer.DGEList html rowsum html scaleOffset html spliceVariants html splitIntoGroups html subsetting html sumTechReps html systematicSubset html thinCounts html topSpliceDGE html topTags html validDGEList html voomLmFit html weightedCondLogLikDerDelta html zscoreNBinom html ** building package indices ** installing vignettes 'edgeR.Rnw' ** 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 install for x64 * installing *source* package 'edgeR' ... ** libs "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_add_prior_count.cpp -o R_add_prior_count.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_ave_log_cpm.cpp -o R_ave_log_cpm.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_calculate_cpm.cpp -o R_calculate_cpm.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_check_poisson_bound.cpp -o R_check_poisson_bound.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_compute_apl.cpp -o R_compute_apl.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_compute_nbdev.cpp -o R_compute_nbdev.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_exact_test_by_deviance.cpp -o R_exact_test_by_deviance.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_fit_levenberg.cpp -o R_fit_levenberg.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_fit_one_group.cpp -o R_fit_one_group.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_get_one_way_fitted.cpp -o R_get_one_way_fitted.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_initialize_levenberg.cpp -o R_initialize_levenberg.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_loess_by_col.cpp -o R_loess_by_col.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_maximize_interpolant.cpp -o R_maximize_interpolant.o "C:/rtools40/mingw64/bin/"gcc -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -std=gnu99 -mfpmath=sse -msse2 -mstackrealign -c R_process_hairpin_reads.c -o R_process_hairpin_reads.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c R_simple_good_turing.cpp -o R_simple_good_turing.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c add_prior.cpp -o add_prior.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c adj_coxreid.cpp -o adj_coxreid.o "C:/rtools40/mingw64/bin/"gcc -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -std=gnu99 -mfpmath=sse -msse2 -mstackrealign -c fmm_spline.c -o fmm_spline.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c glm_levenberg.cpp -o glm_levenberg.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c glm_one_group.cpp -o glm_one_group.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c init.cpp -o init.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c interpolator.cpp -o interpolator.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c nbdev.cpp -o nbdev.o "C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/Rcpp/include' -I"C:/extsoft/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c objects.cpp -o objects.o C:/rtools40/mingw64/bin/g++ -shared -s -static-libgcc -o edgeR.dll tmp.def R_add_prior_count.o R_ave_log_cpm.o R_calculate_cpm.o R_check_poisson_bound.o R_compute_apl.o R_compute_nbdev.o R_exact_test_by_deviance.o R_fit_levenberg.o R_fit_one_group.o R_get_one_way_fitted.o R_initialize_levenberg.o R_loess_by_col.o R_maximize_interpolant.o R_process_hairpin_reads.o R_simple_good_turing.o add_prior.o adj_coxreid.o fmm_spline.o glm_levenberg.o glm_one_group.o init.o interpolator.o nbdev.o objects.o -LC:/Users/BIOCBU~1/BBS-3~1.13-/R/bin/x64 -lRlapack -LC:/Users/BIOCBU~1/BBS-3~1.13-/R/bin/x64 -lRblas -lgfortran -lm -lquadmath -LC:/extsoft/lib/x64 -LC:/extsoft/lib -LC:/Users/BIOCBU~1/BBS-3~1.13-/R/bin/x64 -lR installing to C:/Users/biocbuild/bbs-3.13-bioc/meat/edgeR.buildbin-libdir/edgeR/libs/x64 ** testing if installed package can be loaded * MD5 sums packaged installation of 'edgeR' as edgeR_3.34.1.zip * DONE (edgeR) * installing to library 'C:/Users/biocbuild/bbs-3.13-bioc/R/library' package 'edgeR' successfully unpacked and MD5 sums checked
edgeR.Rcheck/tests_i386/edgeR-Tests.Rout.save R version 4.1.0 beta (2021-05-09 r80275) -- "Camp Pontanezen" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale 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 <- calcNormFactors(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 $AveLogCPM [1] 13.561832 9.682757 10.447014 10.532113 10.452489 995 more elements ... > > calcNormFactors(d$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 $n0 [1] 0 > > > > proc.time() user system elapsed 1.90 0.15 2.06 |
edgeR.Rcheck/tests_x64/edgeR-Tests.Rout.save R version 4.1.0 beta (2021-05-09 r80275) -- "Camp Pontanezen" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(edgeR) Loading required package: limma > options(warnPartialMatchArgs=TRUE,warnPartialMatchAttr=TRUE,warnPartialMatchDollar=TRUE) > > set.seed(0); u <- runif(100) > > # generate raw counts from NB, create list object > y <- matrix(rnbinom(80,size=5,mu=10),nrow=20) > y <- rbind(0,c(0,0,2,2),y) > rownames(y) <- paste("Tag",1:nrow(y),sep=".") > d <- DGEList(counts=y,group=rep(1:2,each=2),lib.size=1001:1004) > > filterByExpr(d) Tag.1 Tag.2 Tag.3 Tag.4 Tag.5 Tag.6 Tag.7 Tag.8 Tag.9 Tag.10 Tag.11 FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE Tag.12 Tag.13 Tag.14 Tag.15 Tag.16 Tag.17 Tag.18 Tag.19 Tag.20 Tag.21 Tag.22 TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE > > # estimate common dispersion and find differences in expression > d <- estimateCommonDisp(d) > d$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 <- calcNormFactors(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 $AveLogCPM [1] 13.561832 9.682757 10.447014 10.532113 10.452489 995 more elements ... > > calcNormFactors(d$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 $n0 [1] 0 > > > > proc.time() user system elapsed 1.90 0.15 2.06 |
edgeR.Rcheck/tests_i386/edgeR-Tests.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(edgeR) Loading required package: limma > options(warnPartialMatchArgs=TRUE,warnPartialMatchAttr=TRUE,warnPartialMatchDollar=TRUE) > > set.seed(0); u <- runif(100) > > # generate raw counts from NB, create list object > y <- matrix(rnbinom(80,size=5,mu=10),nrow=20) > y <- rbind(0,c(0,0,2,2),y) > rownames(y) <- paste("Tag",1:nrow(y),sep=".") > d <- DGEList(counts=y,group=rep(1:2,each=2),lib.size=1001:1004) > > filterByExpr(d) Tag.1 Tag.2 Tag.3 Tag.4 Tag.5 Tag.6 Tag.7 Tag.8 Tag.9 Tag.10 Tag.11 FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE Tag.12 Tag.13 Tag.14 Tag.15 Tag.16 Tag.17 Tag.18 Tag.19 Tag.20 Tag.21 Tag.22 TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE > > # estimate common dispersion and find differences in expression > d <- estimateCommonDisp(d) > d$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 <- calcNormFactors(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 $AveLogCPM [1] 13.561832 9.682757 10.447014 10.532113 10.452489 995 more elements ... > > calcNormFactors(d$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 $n0 [1] 0 > > > > proc.time() user system elapsed 3.62 0.12 3.73 |
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edgeR.Rcheck/tests_x64/edgeR-Tests.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(edgeR) Loading required package: limma > options(warnPartialMatchArgs=TRUE,warnPartialMatchAttr=TRUE,warnPartialMatchDollar=TRUE) > > set.seed(0); u <- runif(100) > > # generate raw counts from NB, create list object > y <- matrix(rnbinom(80,size=5,mu=10),nrow=20) > y <- rbind(0,c(0,0,2,2),y) > rownames(y) <- paste("Tag",1:nrow(y),sep=".") > d <- DGEList(counts=y,group=rep(1:2,each=2),lib.size=1001:1004) > > filterByExpr(d) Tag.1 Tag.2 Tag.3 Tag.4 Tag.5 Tag.6 Tag.7 Tag.8 Tag.9 Tag.10 Tag.11 FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE Tag.12 Tag.13 Tag.14 Tag.15 Tag.16 Tag.17 Tag.18 Tag.19 Tag.20 Tag.21 Tag.22 TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE > > # estimate common dispersion and find differences in expression > d <- estimateCommonDisp(d) > d$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 <- calcNormFactors(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 $AveLogCPM [1] 13.561832 9.682757 10.447014 10.532113 10.452489 995 more elements ... > > calcNormFactors(d$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 > > > > proc.time() user system elapsed 3.75 0.17 3.90 |
edgeR.Rcheck/examples_i386/edgeR-Ex.timings
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edgeR.Rcheck/examples_x64/edgeR-Ex.timings
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