Yunshun Chen
| Snapshot Date: 2019-04-12 17:01:30 -0400 (Fri, 12 Apr 2019) |
| URL: https://git.bioconductor.org/packages/edgeR |
| Branch: RELEASE_3_8 |
| Last Commit: d1260a2 |
| Last Changed Date: 2019-01-01 19:02:19 -0400 (Tue, 01 Jan 2019) |
| Back to Multiple platform build/check report for BioC 3.8 |
|
This page was generated on 2019-04-13 11:20:47 -0400 (Sat, 13 Apr 2019).
| Package 458/1649 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||
| edgeR 3.24.3 Yunshun Chen
| malbec1 | Linux (Ubuntu 16.04.6 LTS) / x86_64 | OK | OK | OK | | ||||||
| tokay1 | Windows Server 2012 R2 Standard / x64 | OK | OK | [ OK ] | OK | | ||||||
| merida1 | OS X 10.11.6 El Capitan / x86_64 | OK | OK | OK | OK | |
| Package: edgeR |
| Version: 3.24.3 |
| Command: C:\Users\biocbuild\bbs-3.8-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:edgeR.install-out.txt --library=C:\Users\biocbuild\bbs-3.8-bioc\R\library --no-vignettes --timings edgeR_3.24.3.tar.gz |
| StartedAt: 2019-04-13 02:00:20 -0400 (Sat, 13 Apr 2019) |
| EndedAt: 2019-04-13 02:03:04 -0400 (Sat, 13 Apr 2019) |
| EllapsedTime: 163.8 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: edgeR.Rcheck |
| Warnings: 0 |
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###
### Running command:
###
### C:\Users\biocbuild\bbs-3.8-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:edgeR.install-out.txt --library=C:\Users\biocbuild\bbs-3.8-bioc\R\library --no-vignettes --timings edgeR_3.24.3.tar.gz
###
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* using log directory 'C:/Users/biocbuild/bbs-3.8-bioc/meat/edgeR.Rcheck'
* using R version 3.5.3 (2019-03-11)
* 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 '3.24.3'
* checking package namespace information ... OK
* checking package dependencies ...Warning: unable to access index for repository https://CRAN.R-project.org/src/contrib:
cannot open URL 'https://CRAN.R-project.org/src/contrib/PACKAGES'
OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'edgeR' can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking 'build' directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* loading checks for arch 'i386'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* 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 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-3.8-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)
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
** running examples for arch 'x64' ... OK
Examples with CPU or elapsed time > 5s
user system elapsed
romer.DGEList 5.01 0 5.02
* 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: 1 NOTE
See
'C:/Users/biocbuild/bbs-3.8-bioc/meat/edgeR.Rcheck/00check.log'
for details.
edgeR.Rcheck/00install.out
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###
### Running command:
###
### C:\cygwin\bin\curl.exe -O https://malbec1.bioconductor.org/BBS/3.8/bioc/src/contrib/edgeR_3.24.3.tar.gz && rm -rf edgeR.buildbin-libdir && mkdir edgeR.buildbin-libdir && C:\Users\biocbuild\bbs-3.8-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=edgeR.buildbin-libdir edgeR_3.24.3.tar.gz && C:\Users\biocbuild\bbs-3.8-bioc\R\bin\R.exe CMD INSTALL edgeR_3.24.3.zip && rm edgeR_3.24.3.tar.gz edgeR_3.24.3.zip
###
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% Total % Received % Xferd Average Speed Time Time Time Current
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100 1396k 100 1396k 0 0 29.9M 0 --:--:-- --:--:-- --:--:-- 32.4M
install for i386
* installing *source* package 'edgeR' ...
** libs
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_add_prior_count.cpp -o R_add_prior_count.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_ave_log_cpm.cpp -o R_ave_log_cpm.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_calculate_cpm.cpp -o R_calculate_cpm.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_check_poisson_bound.cpp -o R_check_poisson_bound.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_compute_apl.cpp -o R_compute_apl.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_compute_nbdev.cpp -o R_compute_nbdev.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_exact_test_by_deviance.cpp -o R_exact_test_by_deviance.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_fit_levenberg.cpp -o R_fit_levenberg.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_fit_one_group.cpp -o R_fit_one_group.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_get_one_way_fitted.cpp -o R_get_one_way_fitted.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_initialize_levenberg.cpp -o R_initialize_levenberg.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_loess_by_col.cpp -o R_loess_by_col.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_maximize_interpolant.cpp -o R_maximize_interpolant.o
C:/Rtools/mingw_32/bin/gcc -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O3 -Wall -std=gnu99 -mtune=generic -c R_process_hairpin_reads.c -o R_process_hairpin_reads.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_simple_good_turing.cpp -o R_simple_good_turing.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c add_prior.cpp -o add_prior.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c adj_coxreid.cpp -o adj_coxreid.o
C:/Rtools/mingw_32/bin/gcc -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O3 -Wall -std=gnu99 -mtune=generic -c fmm_spline.c -o fmm_spline.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c glm_levenberg.cpp -o glm_levenberg.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c glm_one_group.cpp -o glm_one_group.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c init.cpp -o init.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c interpolator.cpp -o interpolator.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c nbdev.cpp -o nbdev.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c objects.cpp -o objects.o
C:/Rtools/mingw_32/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˜1.8-B/R/bin/i386 -lRlapack -LC:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/bin/i386 -lRblas -lgfortran -lm -lquadmath -LC:/extsoft/lib/i386 -LC:/extsoft/lib -LC:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/bin/i386 -lR
installing to C:/Users/biocbuild/bbs-3.8-bioc/meat/edgeR.buildbin-libdir/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
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
equalizeLibSizes html
estimateCommonDisp html
estimateDisp html
estimateExonGenewisedisp html
estimateGLMCommonDisp html
estimateGLMRobustDisp html
estimateGLMTagwiseDisp html
estimateGLMTrendedDisp html
estimateTagwiseDisp html
estimateTrendedDisp html
exactTest html
expandAsMatrix html
filterByExpr html
getCounts html
getPriorN html
gini html
glmQLFTest html
glmTreat html
glmfit html
goana html
gof html
goodTuring 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
weightedCondLogLikDerDelta html
zscoreNBinom html
** building package indices
** installing vignettes
'edgeR.Rnw'
** testing if installed package can be loaded
In R CMD INSTALL
install for x64
* installing *source* package 'edgeR' ...
** libs
C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_add_prior_count.cpp -o R_add_prior_count.o
C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_ave_log_cpm.cpp -o R_ave_log_cpm.o
C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_calculate_cpm.cpp -o R_calculate_cpm.o
C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_check_poisson_bound.cpp -o R_check_poisson_bound.o
C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_compute_apl.cpp -o R_compute_apl.o
C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_compute_nbdev.cpp -o R_compute_nbdev.o
C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_exact_test_by_deviance.cpp -o R_exact_test_by_deviance.o
C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_fit_levenberg.cpp -o R_fit_levenberg.o
C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_fit_one_group.cpp -o R_fit_one_group.o
C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_get_one_way_fitted.cpp -o R_get_one_way_fitted.o
C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_initialize_levenberg.cpp -o R_initialize_levenberg.o
C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_loess_by_col.cpp -o R_loess_by_col.o
C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c R_maximize_interpolant.cpp -o R_maximize_interpolant.o
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C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c adj_coxreid.cpp -o adj_coxreid.o
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C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c glm_one_group.cpp -o glm_one_group.o
C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c init.cpp -o init.o
C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c interpolator.cpp -o interpolator.o
C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c nbdev.cpp -o nbdev.o
C:/Rtools/mingw_64/bin/g++ -std=gnu++11 -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/Users/biocbuild/bbs-3.8-bioc/R/library/Rcpp/include" -I"C:/extsoft/include" -O2 -Wall -mtune=generic -c objects.cpp -o objects.o
C:/Rtools/mingw_64/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˜1.8-B/R/bin/x64 -lRlapack -LC:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/bin/x64 -lRblas -lgfortran -lm -lquadmath -LC:/extsoft/lib/x64 -LC:/extsoft/lib -LC:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/bin/x64 -lR
installing to C:/Users/biocbuild/bbs-3.8-bioc/meat/edgeR.buildbin-libdir/edgeR/libs/x64
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'edgeR' as edgeR_3.24.3.zip
* DONE (edgeR)
In R CMD INSTALL
In R CMD INSTALL
* installing to library 'C:/Users/biocbuild/bbs-3.8-bioc/R/library'
package 'edgeR' successfully unpacked and MD5 sums checked
In R CMD INSTALL
|
edgeR.Rcheck/tests_i386/edgeR-Tests.Rout
R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 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
>
> 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$tagwise.dispersion)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.1005 0.1629 0.2064 0.2077 0.2585 0.3164
> de <- exactTest(d2)
> topTags(de)
Comparison of groups: 2-1
logFC logCPM PValue FDR
Tag.17 2.0450951 13.73726 0.02427872 0.5341319
Tag.21 -1.7265927 13.38327 0.05234833 0.5758316
Tag.6 -1.6330014 12.81479 0.12846308 0.8954397
Tag.2 4.0861092 11.54121 0.16280722 0.8954397
Tag.16 0.9324887 13.57074 0.24308201 0.9711975
Tag.20 0.8543044 13.76364 0.35534649 0.9711975
Tag.19 -0.7976535 13.31405 0.38873717 0.9711975
Tag.3 -0.7300525 13.54155 0.40001438 0.9711975
Tag.12 0.7080985 14.31389 0.43530227 0.9711975
Tag.8 -0.7918376 12.86353 0.49782701 0.9711975
>
> summary(exactTest(d2,rejection="smallp")$table$PValue)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.02428 0.36369 0.55662 0.54319 0.78889 1.00000
> summary(exactTest(d2,rejection="deviance")$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$common.dispersion,prior.count=0.5)
> summary(fit$coef)
(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.
0.1423 0.1618 0.1788 0.1863 0.2015 0.2692
> 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.
0.1423 0.1605 0.1783 0.1867 0.2031 0.2740
>
> # 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)
> 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
$AveLogCPM
[1] 13.561832 9.682757 10.447014 10.532113 10.452489
995 more elements ...
>
> d2 <- estimateDisp(d, design)
> summary(d2$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$coef)
(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
$n0
[1] 0
>
>
>
> proc.time()
user system elapsed
4.10 0.07 4.17
|
edgeR.Rcheck/tests_x64/edgeR-Tests.Rout
R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(edgeR)
Loading required package: limma
>
> set.seed(0); u <- runif(100)
>
> # generate raw counts from NB, create list object
> y <- matrix(rnbinom(80,size=5,mu=10),nrow=20)
> y <- rbind(0,c(0,0,2,2),y)
> rownames(y) <- paste("Tag",1:nrow(y),sep=".")
> d <- DGEList(counts=y,group=rep(1:2,each=2),lib.size=1001:1004)
>
> filterByExpr(d)
Tag.1 Tag.2 Tag.3 Tag.4 Tag.5 Tag.6 Tag.7 Tag.8 Tag.9 Tag.10 Tag.11
FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE
Tag.12 Tag.13 Tag.14 Tag.15 Tag.16 Tag.17 Tag.18 Tag.19 Tag.20 Tag.21 Tag.22
TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE
>
> # estimate common dispersion and find differences in expression
> d <- estimateCommonDisp(d)
> d$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$tagwise.dispersion)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.1005 0.1629 0.2064 0.2077 0.2585 0.3164
> de <- exactTest(d2)
> topTags(de)
Comparison of groups: 2-1
logFC logCPM PValue FDR
Tag.17 2.0450951 13.73726 0.02427872 0.5341319
Tag.21 -1.7265927 13.38327 0.05234833 0.5758316
Tag.6 -1.6330014 12.81479 0.12846308 0.8954397
Tag.2 4.0861092 11.54121 0.16280722 0.8954397
Tag.16 0.9324887 13.57074 0.24308201 0.9711975
Tag.20 0.8543044 13.76364 0.35534649 0.9711975
Tag.19 -0.7976535 13.31405 0.38873717 0.9711975
Tag.3 -0.7300525 13.54155 0.40001438 0.9711975
Tag.12 0.7080985 14.31389 0.43530227 0.9711975
Tag.8 -0.7918376 12.86353 0.49782701 0.9711975
>
> summary(exactTest(d2,rejection="smallp")$table$PValue)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.02428 0.36369 0.55662 0.54319 0.78889 1.00000
> summary(exactTest(d2,rejection="deviance")$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$common.dispersion,prior.count=0.5)
> summary(fit$coef)
(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.
0.1423 0.1618 0.1788 0.1863 0.2015 0.2692
> 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.
0.1423 0.1605 0.1783 0.1867 0.2031 0.2740
>
> # 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)
> 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
$AveLogCPM
[1] 13.561832 9.682757 10.447014 10.532113 10.452489
995 more elements ...
>
> d2 <- estimateDisp(d, design)
> summary(d2$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$coef)
(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
$n0
[1] 0
>
>
>
> proc.time()
user system elapsed
3.28 0.09 3.36
|
|
edgeR.Rcheck/tests_i386/edgeR-Tests.Rout.save
R version 3.5.1 (2018-07-02) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(edgeR)
Loading required package: limma
>
> set.seed(0); u <- runif(100)
>
> # generate raw counts from NB, create list object
> y <- matrix(rnbinom(80,size=5,mu=10),nrow=20)
> y <- rbind(0,c(0,0,2,2),y)
> rownames(y) <- paste("Tag",1:nrow(y),sep=".")
> d <- DGEList(counts=y,group=rep(1:2,each=2),lib.size=1001:1004)
>
> filterByExpr(d)
Tag.1 Tag.2 Tag.3 Tag.4 Tag.5 Tag.6 Tag.7 Tag.8 Tag.9 Tag.10 Tag.11
FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE
Tag.12 Tag.13 Tag.14 Tag.15 Tag.16 Tag.17 Tag.18 Tag.19 Tag.20 Tag.21 Tag.22
TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE
>
> # estimate common dispersion and find differences in expression
> d <- estimateCommonDisp(d)
> d$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$tagwise.dispersion)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.1005 0.1629 0.2064 0.2077 0.2585 0.3164
> de <- exactTest(d2)
> topTags(de)
Comparison of groups: 2-1
logFC logCPM PValue FDR
Tag.17 2.0450951 13.73726 0.02427872 0.5341319
Tag.21 -1.7265927 13.38327 0.05234833 0.5758316
Tag.6 -1.6330014 12.81479 0.12846308 0.8954397
Tag.2 4.0861092 11.54121 0.16280722 0.8954397
Tag.16 0.9324887 13.57074 0.24308201 0.9711975
Tag.20 0.8543044 13.76364 0.35534649 0.9711975
Tag.19 -0.7976535 13.31405 0.38873717 0.9711975
Tag.3 -0.7300525 13.54155 0.40001438 0.9711975
Tag.12 0.7080985 14.31389 0.43530227 0.9711975
Tag.8 -0.7918376 12.86353 0.49782701 0.9711975
>
> summary(exactTest(d2,rejection="smallp")$table$PValue)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.02428 0.36369 0.55662 0.54319 0.78889 1.00000
> summary(exactTest(d2,rejection="deviance")$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$common.dispersion,prior.count=0.5)
> summary(fit$coef)
(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.
0.1423 0.1618 0.1788 0.1863 0.2015 0.2692
> 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.
0.1423 0.1605 0.1783 0.1867 0.2031 0.2740
>
> # 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)
> 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
$AveLogCPM
[1] 13.561832 9.682757 10.447014 10.532113 10.452489
995 more elements ...
>
> d2 <- estimateDisp(d, design)
> summary(d2$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$coef)
(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
$n0
[1] 0
>
>
>
> proc.time()
user system elapsed
6.46 0.42 7.00
|
|
|
edgeR.Rcheck/tests_x64/edgeR-Tests.Rout.save
R version 3.5.1 (2018-07-02) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(edgeR)
Loading required package: limma
>
> set.seed(0); u <- runif(100)
>
> # generate raw counts from NB, create list object
> y <- matrix(rnbinom(80,size=5,mu=10),nrow=20)
> y <- rbind(0,c(0,0,2,2),y)
> rownames(y) <- paste("Tag",1:nrow(y),sep=".")
> d <- DGEList(counts=y,group=rep(1:2,each=2),lib.size=1001:1004)
>
> filterByExpr(d)
Tag.1 Tag.2 Tag.3 Tag.4 Tag.5 Tag.6 Tag.7 Tag.8 Tag.9 Tag.10 Tag.11
FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE
Tag.12 Tag.13 Tag.14 Tag.15 Tag.16 Tag.17 Tag.18 Tag.19 Tag.20 Tag.21 Tag.22
TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE
>
> # estimate common dispersion and find differences in expression
> d <- estimateCommonDisp(d)
> d$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$tagwise.dispersion)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.1005 0.1629 0.2064 0.2077 0.2585 0.3164
> de <- exactTest(d2)
> topTags(de)
Comparison of groups: 2-1
logFC logCPM PValue FDR
Tag.17 2.0450951 13.73726 0.02427872 0.5341319
Tag.21 -1.7265927 13.38327 0.05234833 0.5758316
Tag.6 -1.6330014 12.81479 0.12846308 0.8954397
Tag.2 4.0861092 11.54121 0.16280722 0.8954397
Tag.16 0.9324887 13.57074 0.24308201 0.9711975
Tag.20 0.8543044 13.76364 0.35534649 0.9711975
Tag.19 -0.7976535 13.31405 0.38873717 0.9711975
Tag.3 -0.7300525 13.54155 0.40001438 0.9711975
Tag.12 0.7080985 14.31389 0.43530227 0.9711975
Tag.8 -0.7918376 12.86353 0.49782701 0.9711975
>
> summary(exactTest(d2,rejection="smallp")$table$PValue)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.02428 0.36369 0.55662 0.54319 0.78889 1.00000
> summary(exactTest(d2,rejection="deviance")$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$common.dispersion,prior.count=0.5)
> summary(fit$coef)
(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.
0.1423 0.1618 0.1788 0.1863 0.2015 0.2692
> 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.
0.1423 0.1605 0.1783 0.1867 0.2031 0.2740
>
> # 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)
> 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
$AveLogCPM
[1] 13.561832 9.682757 10.447014 10.532113 10.452489
995 more elements ...
>
> d2 <- estimateDisp(d, design)
> summary(d2$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$coef)
(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
6.46 0.42 7.00
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edgeR.Rcheck/examples_i386/edgeR-Ex.timings
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edgeR.Rcheck/examples_x64/edgeR-Ex.timings
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