A scaling normalization method for differential expression analysis of RNA-seq data

The transcriptional architecture is a complex and dynamic aspect of a cell's function. Next generation sequencing of steady state RNA (RNA-seq) gives unprecedented detail about the RNA landscape within a cell. Not only can expression levels of genes be interrogated without specific prior knowledge, but comparisons of expression levels between genes within a sample can be made. It has also been demonstrated that splicing variants [1,2] and single nucleotide polymorphisms [3] can be detected through sequencing the transcriptome, opening up the opportunity to interrogate allele-specific expression and RNA editing.An important aspect of dealing with the vast amounts of data generated from short read sequencing is the processing methods used to extract and interpret the information. Experience with microarray data has repeatedly shown that normalization is a critical component of the processing pipeline, allowing accurate estimation and detection of differential expression (DE) [4]. The aim of normalization is to remove systematic technical effects that occur in the data to ensure that technical bias has minimal impact on the results. However, the procedure for generating RNA-seq data is fundamentally different from that for microarray data, so the normalization methods used are not directly applicable. It has been suggested that 'One particularly powerful advantage of RNA-seq is that it can capture transcriptome dynamics across different tissues or conditions without sophisticated normalization of data sets' [5]. We demonstrate here that the reality of RNA-seq data analysis is not this simple; normalization is often still an important consideration.Current RNA-seq analysis methods typically standardize data between samples by scaling the number of reads in a given lane or library to a common value across all sequenced libraries in the experiment. For example, several authors have modeled the observed counts for a gene with a mean that includes a factor for the total num