Dear Professo Thanks for the clarification. I'd like to use that method as a starting list for do some enrichment analisys using SPIA. res <-results(dds) ddsNoPrior <- DESeq(ddHTSeq, betaPrior=FALSE) #only for lessABs resGA <- results(ddsNoPrior, lfcThreshold=.5, altHypothesis="lessAbs") #greater tdi resGA2 <- results(dds, lfcThreshold=1, altHypothesis="greaterAbs") #greater tdi If I try to see what change in logfoldchange in this methods I have this results: table(resGA2$log2FoldChange == res$log2FoldChange )
TRUE 54927 ie. resGA2$log2FoldChange[1] [1] 0.2104923 > res$log2FoldChange[1] [1] 0.2104923 So What is wrong? I'm interest to have a list of genes are greater than 1 and use as de.genes for the spia package. Here don't found difference!! thanks in advance for kind help and patience! jarod >----Messaggio originale---- >Da: and...@embl.de >Data: 12/07/2014 10.41 >A: "Bioconductor List"<bioconduc...@r-project.org> >Cc: <jarod...@libero.it> >Ogg: Re: Deseq2 and differentia expression > >[Reposting from bioc-devel to bioconductor, where this mail should have freater than 1 and use as de.genes for the spia package. Here don't found difference. >gone to] > >Hi Jarod > >Mike overlooked one point in your question > >On 11/07/14 12:05, jarod...@libero.it wrote: >> If I interested in the genes that have a foldchange more than 0.5 and 2 I need >> to use this comand is it right? > >DESeq2 reports al fold changes on a log2 scale. So your limits of 0.5 >and 2 for unlogarithmized fold changes translate to -1 and +1 on the >log2 scale (because 2^(-1)=0.5 and 2^1 = 1, with '^' meaning 'to the >power of'). > >Also, the 'lfcThreshold' parameter wants an _absolute_ log fold change. >Hence, you want: > >ddsNoPrior <- DESeq(ddHTSeq, betaPrior=FALSE) >res <- results(ddsNoPrior, lfcThreshold=1, altHypothesis="greaterAbs") > >to get a list of all genes with an absolute log2 fold change greater >than 1, i.e., all genes with a log2 fold change greater than 1 or less >than -1, i.e., all genes with fold change below 0.5 or above 2. > >Then, in the results table, look at the log2FoldChange column to see >which genes went up and which went down. > > Simon > _______________________________________________ Bioc-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/bioc-devel
