hi Jarod, Please take a look at the beginner vignette for DESeq2. We explain a lot of the questions you are asking, including how to subset the object based on adjusted p-value. Please take a look a my previous email as well, I demonstrated how to subset the results object. As you can see in the vignette R output, the rownames of the object returned by results inherits the rownames of dds. So this gives the gene names:
rownames(res) Mike On Fri, Jul 11, 2014 at 10:55 AM, jarod...@libero.it <jarod...@libero.it> wrote: > Dear All I don't understand. I want to extract only genes have a fold-change > more than 0.5 > I use this comand but I have all the genes inside: > resGA2 <- results(dds, lfcThreshold=.5, altHypothesis="greaterAbs") #greater > dim(resGA) >>> [1] 62893 6 >>>> dim(resGA2) >>> [1] 62893 6 >>>> dim(resGA3) >>> [1] 62893 6 >>> > how can extract the names of genes are in resGA2? > thanks for the patience! > j. > > >>----Messaggio originale---- >>Da: michaelisaiahl...@gmail.com >>Data: 11/07/2014 15.15 >>A: "jarod...@libero.it"<jarod...@libero.it> >>Cc: "bioc-devel@r-project.org"<bioc-devel@r-project.org> >>Ogg: Re: Re: [Bioc-devel] Deseq2 and differentia expression >> >>hi Jarod, >> >>This is more of a main Bioc mailing list question, so you can address >>future questions there. >> >>On Fri, Jul 11, 2014 at 6:05 AM, jarod...@libero.it <jarod...@libero.it> > wrote: >>> 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? >> >>the second and third results() commands below give you this. >> >>> 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! >>> >> >>DESeq2 returns the results for all the genes in the same order as the >>original object. You need to specify a threshold on adjusted p-value. >> >>table(res$padj < 0.1) >> >>You can use subset(res, padj < 0.1) to filter the DataFrame. >> >>> >>>>----Messaggio originale---- >>>>Da: michaelisaiahl...@gmail.com >>>>Data: 10/07/2014 14.46 >>>>A: "jarod...@libero.it"<jarod...@libero.it> >>>>Cc: "bioc-devel@r-project.org"<bioc-devel@r-project.org> >>>>Ogg: Re: [Bioc-devel] Deseq2 and differentia expression >>>> >>>>hi Jarod, >>>> >>>>On Thu, Jul 10, 2014 at 7:59 AM, jarod...@libero.it <jarod...@libero.it> >>> wrote: >>>>> Hi there!!! >>>>> >>>>> I have did this code: >>>>> SampleTable <-data.frame(SampleName=metadata$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 _______________________________________________ Bioc-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/bioc-devel
