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
