I used nbinomGLMTest to compare the reduced model and full model given as below, and I got some zero for p values. Is this possible for nbinomGLMTest? If so, what's the potential reason? Thanks Full model: full<-fitNbinomGLMs(cdsFull,count~Line+Rep+Sex) Reduced model: reduced<-fitNbinomGLMs(cdsFull,count~Line+Rep) -- output of sessionInfo(): sessionInfo() R version 3.0.1 (2013-05-16) Platform: x86_64-w64-mingw32/x64 (64-bit) locale: [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252 [4] LC_NUMERIC=C LC_TIME=English_United States.1252 attached base packages: [1] parallel stats graphics grDevices utils datasets methods base other attached packages: [1] DESeq_1.12.1 lattice_0.20-15 locfit_1.5-9.1 Biobase_2.20.1 BiocGenerics_0.6.0 edgeR_3.2.4 limma_3.16.8 loaded via a namespace (and not attached): [1] annotate_1.38.0 AnnotationDbi_1.22.6 DBI_0.2-7 genefilter_1.42.0 geneplotter_1.38.0 grid_3.0.1 [7] IRanges_1.18.4 RColorBrewer_1.0-5 RSQLite_0.11.4 splines_3.0.1 stats4_3.0.1 survival_2.37-4 [13] XML_3.98-1.1 xtable_1.7-1 -- Sent via the guest posting facility at bioconductor.org.

Dear Yanzhu one potential explanation is that the model with sex fits your data a lot better than without sex, for some genes, and that a small p-value results. Note that R (and hence DESeq) uses floating-point arithmetic, so a range of small numbers (on the real axis) are all represented by the same value (in R) of 0. It is not unusual that expression for some genes shows a strong sex effect. For instance, if you are looking at data from a mammal, it is plausible that the X and Y chromosomes contain such genes. Kind regards Wolfgang Il giorno 07 Feb 2014, alle ore 17:51, Yanzhu [guest] <guest at="" bioconductor.org=""> ha scritto: > > I used nbinomGLMTest to compare the reduced model and full model given as below, and I got some zero for p values. Is this possible for nbinomGLMTest? If so, what's the potential reason? Thanks > > > Full model: > full<-fitNbinomGLMs(cdsFull,count~Line+Rep+Sex) > > Reduced model: > reduced<-fitNbinomGLMs(cdsFull,count~Line+Rep) > > > -- output of sessionInfo(): > > sessionInfo() > R version 3.0.1 (2013-05-16) > Platform: x86_64-w64-mingw32/x64 (64-bit) > > locale: > [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252 > [4] LC_NUMERIC=C LC_TIME=English_United States.1252 > > attached base packages: > [1] parallel stats graphics grDevices utils datasets methods base > > other attached packages: > [1] DESeq_1.12.1 lattice_0.20-15 locfit_1.5-9.1 Biobase_2.20.1 BiocGenerics_0.6.0 edgeR_3.2.4 limma_3.16.8 > > loaded via a namespace (and not attached): > [1] annotate_1.38.0 AnnotationDbi_1.22.6 DBI_0.2-7 genefilter_1.42.0 geneplotter_1.38.0 grid_3.0.1 > [7] IRanges_1.18.4 RColorBrewer_1.0-5 RSQLite_0.11.4 splines_3.0.1 stats4_3.0.1 survival_2.37-4 > [13] XML_3.98-1.1 xtable_1.7-1 > > > > > > > -- > Sent via the guest posting facility at bioconductor.org. > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor