Combining single and paired end RNA-seq data for differential expression analyses

Abstract

Combining RNA-seq data from different platforms should increase the power to detect differentially expressed genes, but may not be straightforward. Here we show how RUVs, a recently published method for removing unwanted variation and normalizing RNA-seq data, can combine the counts of single and paired end read libraries from formalin fixed, paraffin embedded tumor samples to permit differential expression analysis. Seven other intra- or inter-platform normalization methods are also described and the results are compared with those from RUVs.