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? All the data have a FDR less thank 0.1 . : Is it right this comand? res[ which(res$padj < 0.1 ), ] How many significant genes are with FDR less than 0.1 and have an absolute value of foldchange more of 1 ? I have and error on this. I have many NA values. If I try this code I have the follow errors > significant.genes = res[(res$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 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
