Hi , I am using Deseq2 for differential gene expression calculations. I have two conditions Condition a (T) and Condition b (P), when I run the command using the following command lines, I get a long list of differentially expressed gene, more than 1000 unregulated and more than thousand down regulated. I am new to Des, do you think my command lines are good for my experiment and how could I narrow down my differential gene list. I appreciate your time. Thanks in advance, code is : > countsTable <- read.delim ("48_96_filtered.txt", header=TRUE, row.names=1) > pdata = data.frame(condition = factor(c( "T", "T", "T", "P", "P", "P", "P"))) > library (DESeq2) > dds <- DESeqDataSetFromMatrix(countData=countsTable, colData = pdata, design=~condition) colData(dds)$condition <- relevel(colData(dds)$condition, "T") > dds <- DESeq(dds) > results <- results(dds) > results <- as.data.frame(results) > sig.up.results <- results[which(results$padj < 0.05 & results$log2FoldChange > 0),] > sig.down.results <- results[which(results$padj < 0.05 & results$log2FoldChange < 0),] > sig.results <- results[which(results$padj < 0.05),] > sig.results <- sig.results[order(sig.results$log2FoldChange, decreasing=TRUE),] > plotMA(dds, pvalCutoff=0.05) > write.table (sig.results, file= "DESeq2_Sigresultsfiltered_48_96.txt", sep= "\t ", row.names=TRUE, col.names=TRUE) > write.table (results, file= "DESeq2_allresults_filtered_48_96.txt", sep= "\t ", col.names=TRUE) Best regards Syed HHMI-Institute of Neuroscience, 1254 University of Oregon, Eugene, OR 97403-1254

hi Syed, Yes your commands look correct. You say you want to narrow down the gene list. In my previous email I suggested you could filter by effect size (and in DESeq2 v1.4 you can use the LFC threshold argument to the results function). filtering on large, positive effect size would look like this: sig.large.up.results <- results[which(results$padj < 0.05 & results$log2FoldChange > 1),] Mike On Sat, Mar 15, 2014 at 3:08 PM, Mubarak hussain Syed <mosvey@gmail.com>wrote: > Hi , > I am using Deseq2 for differential gene expression calculations. I have > two conditions Condition a (T) and Condition b (P), > when I run the command using the following command lines, I get a long > list of differentially expressed gene, more than 1000 unregulated and > more than thousand down regulated. I am new to Des, do you think my > command lines are good for my experiment and how could I > narrow down my differential gene list. I appreciate your time. Thanks in > advance, code is : > > > countsTable <- read.delim ("48_96_filtered.txt", header=TRUE, > row.names=1) > > pdata = data.frame(condition = factor(c( "T", "T", "T", "P", "P", "P", > "P"))) > > library (DESeq2) > > dds <- DESeqDataSetFromMatrix(countData=countsTable, colData = pdata, > design=~condition) > colData(dds)$condition <- relevel(colData(dds)$condition, "T") > > > dds <- DESeq(dds) > > results <- results(dds) > > results <- as.data.frame(results) > > > sig.up.results <- results[which(results$padj < 0.05 & > results$log2FoldChange > 0),] > > sig.down.results <- results[which(results$padj < 0.05 & > results$log2FoldChange < 0),] > > > sig.results <- results[which(results$padj < 0.05),] > > sig.results <- sig.results[order(sig.results$log2FoldChange, > decreasing=TRUE),] > > > plotMA(dds, pvalCutoff=0.05) > > > write.table (sig.results, file= "DESeq2_Sigresultsfiltered_48_96.txt", > sep= "\t ", row.names=TRUE, col.names=TRUE) > > write.table (results, file= "DESeq2_allresults_filtered_48_96.txt", > sep= "\t ", col.names=TRUE) > > > Best regards > Syed > HHMI-Institute of Neuroscience, > 1254 University of Oregon, > Eugene, OR 97403-1254 > > _______________________________________________ > 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]]