hello all: i run a scipts to deal with my data. every thing is ok, but the last step generate some wired lines rm(list=ls()) library(DESeq) raw.data <- read.table("ordered_sense_raw_v2.csv",row.names=1) counts <- raw.data[, 1:dim(raw.data)[2]] conds = c(rep("col15",3),rep("col1",3),rep("col2h",3),rep("col30",3),r ep("col6h",3),rep("flu15",3),rep("flu1",3),rep("flu2h",3),rep("flu30", 3),rep("flu6h",3)) counts <- counts[rowSums(counts) >= length(conds)/2,] cds <- newCountDataSet( counts, conds ) cds <- estimateSizeFactors(cds) sizeFactors(cds) cds <- estimateDispersions(cds) et <- nbinomTest(cds, "col6h", "flu6h") etSig <- et[et$padj < 0.05, ] etSig have some lines, like this no id id baseMean baseMeanA baseMeanB foldChange log2FoldChange pval padj NA.4649 <na> NA NA NA NA NA NA NA NA.4650 <na> NA NA NA NA NA NA NA NA.4651 <na> NA NA NA NA NA NA NA NA.4652 <na> NA NA NA NA NA NA NA NA.4653 <na> NA NA NA NA NA NA NA NA.4654 <na> NA NA NA NA NA NA NA-- > sessionInfo() R version 2.14.1 (2011-12-22) Platform: x86_64-pc-mingw32/x64 (64-bit) locale: [1] LC_COLLATE=Chinese (Simplified)_People's Republic of China.936 [2] LC_CTYPE=Chinese (Simplified)_People's Republic of China.936 [3] LC_MONETARY=Chinese (Simplified)_People's Republic of China.936 [4] LC_NUMERIC=C [5] LC_TIME=Chinese (Simplified)_People's Republic of China.936 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] DESeq_1.6.1 locfit_1.5-8 Biobase_2.14.0 BiocInstaller_1.2.1 loaded via a namespace (and not attached): [1] annotate_1.32.3 AnnotationDbi_1.16.19 DBI_0.2-5 genefilter_1.36.0 [5] geneplotter_1.32.1 grid_2.14.1 IRanges_1.12.6 lattice_0.20-0 [9] RColorBrewer_1.0-5 RSQLite_0.11.1 splines_2.14.1 survival_2.36-10 [13] tools_2.14.1 xtable_1.7-0 shan gao Room 231(Dr.Fei lab) Boyce Thompson Institute Cornell University Tower Road, Ithaca, NY 14853-1801 Office phone: 1-607-254-1267(day) Official email:sg839 at cornell.edu Facebook:http://www.facebook.com/profile.php?id=100001986532253

>From what you are saying it sounds like you have meaningful data, and rows containing all NAs in the output, try using etSig <- et[ which(et$padj < 0.05), ] to remove them. whichis.na(et$padj < 0.05)) should identify the offending record rows to trace back what is happening in your dataset. Marcus On Sat, Jul 7, 2012 at 7:19 AM, wang peter <wng.peter@gmail.com> wrote: > hello all: > i run a scipts to deal with my data. > every thing is ok, but the last step generate some > wired lines > > rm(list=ls()) > library(DESeq) > raw.data <- read.table("ordered_sense_raw_v2.csv",row.names=1) > counts <- raw.data[, 1:dim(raw.data)[2]] > conds = > c(rep("col15",3),rep("col1",3),rep("col2h",3),rep("col30",3),rep("co l6h",3),rep("flu15",3),rep("flu1",3),rep("flu2h",3),rep("flu30",3),rep ("flu6h",3)) > counts <- counts[rowSums(counts) >= length(conds)/2,] > cds <- newCountDataSet( counts, conds ) > cds <- estimateSizeFactors(cds) > sizeFactors(cds) > cds <- estimateDispersions(cds) > et <- nbinomTest(cds, "col6h", "flu6h") > etSig <- et[et$padj < 0.05, ] > > > etSig have some lines, like this > no id > > id baseMean baseMeanA baseMeanB foldChange log2FoldChange pval > padj > NA.4649 <na> NA NA NA NA NA NA > NA > NA.4650 <na> NA NA NA NA NA NA > NA > NA.4651 <na> NA NA NA NA NA NA > NA > NA.4652 <na> NA NA NA NA NA NA > NA > NA.4653 <na> NA NA NA NA NA NA > NA > NA.4654 <na> NA NA NA NA NA NA > NA-- > > > sessionInfo() > R version 2.14.1 (2011-12-22) > Platform: x86_64-pc-mingw32/x64 (64-bit) > > locale: > [1] LC_COLLATE=Chinese (Simplified)_People's Republic of China.936 > [2] LC_CTYPE=Chinese (Simplified)_People's Republic of China.936 > [3] LC_MONETARY=Chinese (Simplified)_People's Republic of China.936 > [4] LC_NUMERIC=C > [5] LC_TIME=Chinese (Simplified)_People's Republic of China.936 > > attached base packages: > [1] stats graphics grDevices utils datasets methods base > > other attached packages: > [1] DESeq_1.6.1 locfit_1.5-8 Biobase_2.14.0 > BiocInstaller_1.2.1 > > loaded via a namespace (and not attached): > [1] annotate_1.32.3 AnnotationDbi_1.16.19 DBI_0.2-5 > genefilter_1.36.0 > [5] geneplotter_1.32.1 grid_2.14.1 IRanges_1.12.6 > lattice_0.20-0 > [9] RColorBrewer_1.0-5 RSQLite_0.11.1 splines_2.14.1 > survival_2.36-10 > [13] tools_2.14.1 xtable_1.7-0 > > shan gao > Room 231(Dr.Fei lab) > Boyce Thompson Institute > Cornell University > Tower Road, Ithaca, NY 14853-1801 > Office phone: 1-607-254-1267(day) > Official email:sg839@cornell.edu > Facebook:http://www.facebook.com/profile.php?id=100001986532253 > > _______________________________________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > [[alternative HTML version deleted]]

Hi, *if* it's really just a matter of sidestepping some NA values, subset should do the trick quite handily, eg: subset(et, padj < 0.05) HTH, -Steve On Friday, July 6, 2012, wang peter wrote: > hello all: > i run a scipts to deal with my data. > every thing is ok, but the last step generate some > wired lines > > rm(list=ls()) > library(DESeq) > raw.data <- read.table("ordered_sense_raw_v2.csv",row.names=1) > counts <- raw.data[, 1:dim(raw.data)[2]] > conds = > c(rep("col15",3),rep("col1",3),rep("col2h",3),rep("col30",3),rep("co l6h",3),rep("flu15",3),rep("flu1",3),rep("flu2h",3),rep("flu30",3),rep ("flu6h",3)) > counts <- counts[rowSums(counts) >= length(conds)/2,] > cds <- newCountDataSet( counts, conds ) > cds <- estimateSizeFactors(cds) > sizeFactors(cds) > cds <- estimateDispersions(cds) > et <- nbinomTest(cds, "col6h", "flu6h") > etSig <- et[et$padj < 0.05, ] > > > etSig have some lines, like this > no id > > id baseMean baseMeanA baseMeanB foldChange log2FoldChange pval > padj > NA.4649 <na> NA NA NA NA NA NA > NA > NA.4650 <na> NA NA NA NA NA NA > NA > NA.4651 <na> NA NA NA NA NA NA > NA > NA.4652 <na> NA NA NA NA NA NA > NA > NA.4653 <na> NA NA NA NA NA NA > NA > NA.4654 <na> NA NA NA NA NA NA > NA-- > > > sessionInfo() > R version 2.14.1 (2011-12-22) > Platform: x86_64-pc-mingw32/x64 (64-bit) > > locale: > [1] LC_COLLATE=Chinese (Simplified)_People's Republic of China.936 > [2] LC_CTYPE=Chinese (Simplified)_People's Republic of China.936 > [3] LC_MONETARY=Chinese (Simplified)_People's Republic of China.936 > [4] LC_NUMERIC=C > [5] LC_TIME=Chinese (Simplified)_People's Republic of China.936 > > attached base packages: > [1] stats graphics grDevices utils datasets methods base > > other attached packages: > [1] DESeq_1.6.1 locfit_1.5-8 Biobase_2.14.0 > BiocInstaller_1.2.1 > > loaded via a namespace (and not attached): > [1] annotate_1.32.3 AnnotationDbi_1.16.19 DBI_0.2-5 > genefilter_1.36.0 > [5] geneplotter_1.32.1 grid_2.14.1 IRanges_1.12.6 > lattice_0.20-0 > [9] RColorBrewer_1.0-5 RSQLite_0.11.1 splines_2.14.1 > survival_2.36-10 > [13] tools_2.14.1 xtable_1.7-0 > > shan gao > Room 231(Dr.Fei lab) > Boyce Thompson Institute > Cornell University > Tower Road, Ithaca, NY 14853-1801 > Office phone: 1-607-254-1267(day) > Official email:sg839@cornell.edu <javascript:;> > Facebook:http://www.facebook.com/profile.php?id=100001986532253 > > _______________________________________________ > Bioconductor mailing list > Bioconductor@r-project.org <javascript:;> > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > -- Steve Lianoglou Graduate Student: Computational Systems Biology | Memorial Sloan-Kettering Cancer Center | Weill Medical College of Cornell University Contact Info: http://cbio.mskcc.org/~lianos/contact [[alternative HTML version deleted]]