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
