Dear Dr, Thanks so much for clarification!!! So I try the test of log fold change but I'm bit confusion on the results: 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? ddsNoPrior <- DESeq(ddHTSeq, betaPrior=FALSE) #only for lessABs
resGA <- results(ddsNoPrior, lfcThreshold=.5, altHypothesis="lessAbs") #greater tdi resGA2 <- results(dds, lfcThreshold=.5, altHypothesis="greaterAbs") #greater tdi resGA3 <- results(dds, lfcThreshold=2, altHypothesis="greaterAbs") #greater tdi dim(resGA) [1] 62893 6 > dim(resGA2) [1] 62893 6 > dim(resGA3) [1] 62893 6 The number of gene select it is always the same.. Where is my mistake! thanks in advance! >----Messaggio originale---- >Da: michaelisaiahl...@gmail.com >Data: 10/07/2014 14.46 >A: "jarod...@libero.it"<jarod...@libero.it> >Cc: "bioc-devel@r-project.org"<bioc-devel@r-project.org> >Ogg: Re: [Bioc-devel] Deseq2 and differentia expression > >hi Jarod, > >On Thu, Jul 10, 2014 at 7:59 AM, jarod...@libero.it <jarod...@libero.it> wrote: >> Hi there!!! >> >> I have did this code: >> SampleTable <-data.frame(SampleName=metadata$ID_CLINICO, fileName=metadata$NOME, >> condition=metadata$CONDITION,prim=metadata$CDT) >> ddHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable=SampleTable,directory=" >> Count/", design= ~condition) # effetto dello mutazione >> ddHTSeq$condition <- relevel(ddHTSeq$condition, "NVI")# quindi verso non >> viscerali >> dds <- DESeq(ddHTSeq) >> res <-results(dds) >> >> resOrdered <- res[order(res$padj),] >> head(resOrdered) >> ResSig <- res[ which(res$padj < 0.1 ), ] >> >> >> I want to select some data. How can I do? which is the good cut-off on FDR >> values? > >The code above does the selection on adjusted p-value. The right FDR >cutoff is up to you, what percent of false discoveries is tolerable in >the final list of genes? The considerations are: the cost of >validation or following up on a false discovery, versus the cost of a >missed discovery. These are hard to quantify even if you know all the >details of an experiment. > >> All the data have a FDR less thank 0.1 . : >> Is it right this comand? >> res[ which(res$padj < 0.1 ), ] >> > >yes. The which() is necessary because some of the res$padj have NA. If >you have a logical vector with NA, you cannot directly index a >DataFrame, but you can index after calling which(), which will return >the numeric index of the TRUE's. You could also subset with: >subset(res, padj < 0.1). > >The reason for the NAs is explained in the vignette: "Note that some >values in the results table can be set to NA, for either one of the >following reasons:..." > > >> How many significant genes are with FDR less than 0.1 and have an absolute >> value of foldchange more of 1 ? I have and error on this. I have many NA >> values. >> >> If I try this code I have the follow errors >>> significant.genes = res[(res$padj < .05 & abs(res$log2FoldChange) >= 1 ),] # >> Set thethreshold for the log2 fold change. >> Error in normalizeSingleBracketSubscript(i, x, byrow = TRUE, exact = FALSE) : >> subscript contains NAs >> > >This is not the recommended way to filter on large log fold changes. >We have implemented a test specifically for this, check the vignette >section on "Tests of log2 fold change above or below a threshold" > >Mike > >> How can I resolve this problenms? >> thanks in advance for the help >> >> >> >> R version 3.1.0 (2014-04-10) >> Platform: i686-pc-linux-gnu (32-bit) >> >> locale: >> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C >> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 >> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 >> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C >> [9] LC_ADDRESS=C LC_TELEPHONE=C >> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C >> >> attached base packages: >> [1] splines parallel stats graphics grDevices utils datasets >> [8] methods base >> >> other attached packages: >> [1] annotate_1.40.1 RColorBrewer_1.0-5 gplots_2.14.1 >> [4] org.Hs.eg.db_2.10.1 ReportingTools_2.4.0 AnnotationDbi_1.24.0 >> [7] RSQLite_0.11.4 DBI_0.2-7 knitr_1.6 >> [10] biomaRt_2.18.0 DESeq2_1.4.5 RcppArmadillo_0. 4.320.0 >> [13] Rcpp_0.11.2 GenomicRanges_1.14.4 XVector_0.2.0 >> [16] IRanges_1.20.7 affy_1.40.0 NOISeq_2.6.0 >> [19] Biobase_2.22.0 BiocGenerics_0.8.0 >> >> loaded via a namespace (and not attached): >> [1] affyio_1.30.0 AnnotationForge_1.4.4 BiocInstaller_1. >> 12.1 >> [4] Biostrings_2.30.1 biovizBase_1.10.8 bitops_1.0- >> 6 >> [7] BSgenome_1.30.0 Category_2.28.0 caTools_1. >> 17 >> [10] cluster_1.15.2 colorspace_1.2-4 dichromat_2.0- >> 0 >> [13] digest_0.6.4 edgeR_3.4.2 evaluate_0. >> 5.5 >> [16] formatR_0.10 Formula_1.1-1 gdata_2. >> 13.3 >> [19] genefilter_1.44.0 geneplotter_1.40.0 GenomicFeatures_1. >> 14.5 >> [22] ggbio_1.10.16 ggplot2_1.0.0 GO.db_2. >> 10.1 >> [25] GOstats_2.28.0 graph_1.40.1 grid_3. >> 1.0 >> [28] gridExtra_0.9.1 GSEABase_1.24.0 gtable_0. >> 1.2 >> [31] gtools_3.4.1 Hmisc_3.14-4 hwriter_1. >> 3 >> [34] KernSmooth_2.23-12 lattice_0.20-29 latticeExtra_0.6- >> 26 >> [37] limma_3.18.13 locfit_1.5-9.1 MASS_7.3- >> 33 >> [40] Matrix_1.1-4 munsell_0.4.2 PFAM.db_2. >> 10.1 >> [43] plyr_1.8.1 preprocessCore_1.24.0 proto_0.3- >> 10 >> [46] RBGL_1.38.0 RCurl_1.95-4.1 reshape2_1. >> 4 >> [49] R.methodsS3_1.6.1 R.oo_1.18.0 Rsamtools_1. >> 14.3 >> [52] rtracklayer_1.22.7 R.utils_1.32.4 scales_0. >> 2.4 >> [55] stats4_3.1.0 stringr_0.6.2 survival_2.37- >> 7 >> [58] tools_3.1.0 VariantAnnotation_1.8.13 XML_3.98- >> 1.1 >> [61] xtable_1.7-3 zlibbioc_1.8.0 >> >> _______________________________________________ >> Bioc-devel@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/bioc-devel > _______________________________________________ Bioc-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/bioc-devel
