Hi , I have RNA-seq count data for 30,400 genes across 6 conditions (3 replicates per condition). I was trying different normalization methods and then test for differentially expressed genes between conditions. How to test whether estimateSizeFactors() and estimateVarianceFunctions() does a good fit for my data? Also is there a way to test whether the normalization is good ? Any help is greatly appreciated, Thanks, Shrey [[alternative HTML version deleted]]

Hi Shrey > I have RNA-seq count data for 30,400 genes across 6 conditions (3 replicates > per condition). I was trying different normalization methods and then test > for differentially expressed genes between conditions. How to test whether > estimateSizeFactors() and estimateVarianceFunctions() does a good fit for my > data? Also is there a way to test whether the normalization is good ? Any > help is greatly appreciated, To test whether the normalization (i.e., the size factor estimation) worked fine, do an MA plot for a pair of samples and mark the size factor log ratio with a horizontal line. Here is a demonstration with example data: library( DESeq ) # Make some example data (or use your real data ) cds <- makeExampleCountDataSet( ) # estimate the size factors cds <- estimateSizeFactors( cds ) # Choose two samples for which you want to check whether they are # properly normalizae with respect to each other s1 <- 1; s2 <- 2 # Make the MA plot, i.e., plot the log fold change between the sample # against the mean of the log counts plot( ( log10( counts(cds)[,s1] ) + log10( counts(cds)[,s2] ) )/2, log10( counts(cds)[,s2] ) - log10( counts(cds)[,s1] ) ) # In this plot, the bulk of the genes which are not differentially # expressed should scatter around a horizontal line in the middle. # The position of this line should be given by the log ratio of # the size factors. Mark the latter: abline( h=log10( sizeFactors(cds)[s2] ) - log10( sizeFactors(cds)[s1] ), col="red" ) # Now, the red line should go right through the middle of the bulk of # not differentially expressed genes. I hope that helps Simon

Hi Shrey regarding your other question On 12/06/2010 10:54 PM, Shreyartha Mukherjee wrote: > I have RNA-seq count data for 30,400 genes across 6 conditions (3 replicates > per condition). I was trying different normalization methods and then test > for differentially expressed genes between conditions. How to test whether > estimateSizeFactors() and estimateVarianceFunctions() does a good fit for my > data? Also is there a way to test whether the normalization is good ? Any > help is greatly appreciated, To check the effect of estimateVarianceFunctions: have a look at the package vignette, specifically at Fig. 2. There, the variance estimates for each gene are plotted against the mean, and the estimated variance function is indicated by a red line. This should show a reasonable fit. Simon