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During the last 3 years, a number of approaches for the normalization of RNA sequencing data have emerged in the literature, differing both in the type of bias adjustment and in the statistical strategy adopted. However, as data continue to accumulate, there has been no clear consensus on the appropriate normalization method to be used or the impact of a chosen method on the downstream analysis.

Members of the The French StatOmique Consortium have now used a varied group of real and simulated datasets involving different species and experimental designs to perform a comprehensive comparison of seven recently proposed normalization methods for the differential analysis of RNA-seq data

Based on this comparison study, they propose practical recommendations on the appropriate normalization method to be used and its impact on the differential analysis of RNA-seq data. RNA-Seq Data Normalization Methods

1. Total count (TC): Gene counts are divided by the total number of mapped reads (or library size) associated with their lane and multiplied by the mean total count across all the samples of the dataset.
2. Upper Quartile (UQ): Very similar in principle to TC, the total counts are replaced by the upper quartile of counts different from 0 in the computation of the normalization factors.
3. Median (Med): Also similar to TC, the total counts are replaced by the median counts different from 0 in the computation of the normalization factors.
4. DESeq: This normalization method is included in the DESeq Bioconductor package (version 1.6.0) and is based on the hypothesis that most genes are not DE.
5. Trimmed Mean of M-values (TMM): This normalization method is implemented in the edgeR Bioconductor package (version 2.4.0). It is also based on the hypothesis that most genes are not DE.
6. Quantile (Q): First proposed in the context of microarray data, this normalization method consists in matching distributions of gene counts across lanes.
7. Reads Per Kilobase per Million mapped reads (RPKM): This approach was initially introduced to facilitate comparisons between genes within a sample and combines between- and within-sample normalization.

• Dillies MA, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, Keime C, Marot G, Castel D, Estelle J, Guernec G, Jagla B, Jouneau L, Laloë D, Le Gall C, Schaëffer B, Le Crom S, Guedj M, Jaffrézic F; on behalf of The French StatOmique Consortium. (2012) A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform [Epub ahead of print]. [article]

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