Hello, I'm using edgeR for analyzing RNA-seq data containing Control (CR) and two Treatments (HR and SR) with 2 replicates for each. Based on the common dispersion, BCV and the MDS plot, it looks like biological replicates of HR samples does not correlate well. Therefore, the Dispersion value I got is 0.39742 and BCV = 0.6304. Could someone please comment on whether if the dispersion is too large or OK to proceed with DE analysis?. Here is the code I used and my sessionInfo(). Thank you ! Avinash library(edgeR) x <- read.delim(filetxt, row.names=1, stringsAsFactors=FALSE) group = factor(c("CR", "CR", "HR", "HR", "SR", "SR")) y <- DGEList(counts=x, group=group) keep <- rowSums (cpm(y)>1) >= 2 y2 <- y[keep,] colSums(newCountsTable) / 1e06 y2$samples$lib.size <- colSums(y2$counts) y3 <- calcNormFactors (y2) plotMDS(y3) design <- model.matrix(~0+group, data=y3$samples) colnames(design) <- levels(group) design colnames(design) y3 <- estimateGLMCommonDisp(y3, design, verbose=TRUE) #Disp = 0.39742 , BCV = 0.6304 y3 <- estimateGLMTrendedDisp(y3, design) y3 <- estimateGLMTagwiseDisp(y3, design) *sessionInfo()* R version 3.0.2 (2013-09-25) Platform: x86_64-redhat-linux-gnu (64-bit) locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 [6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C 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 methods base other attached packages: [1] BiocInstaller_1.10.4 edgeR_3.4.2 limma_3.17.21 topGO_2.12.0 SparseM_1.03 GO.db_2.9.0 [7] RSQLite_0.11.4 DBI_0.2-7 AnnotationDbi_1.23.18 Biobase_2.21.6 BiocGenerics_0.7.3 graph_1.38.3 [13] plyr_1.8 reshape2_1.2.2 ggplot2_0.9.3.1 [[alternative HTML version deleted]]