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
