A Hypothesis Testing Based Method for Normalization and Differential Expression Analysis of RNA-Seq Data

Next-generation sequencing technologies have made RNA sequencing (RNA-seq) a popular choice for measuring gene expression level. To reduce the noise of gene expression measures and compare them between several conditions or samples, normalization is an essential step to adjust for varying sample sequencing depths and other unwanted technical effects.

Here, researchers from Shenzhen University have developed a novel global scaling normalization method by employing the available knowledge of housekeeping genes. They formulated the problem from the hypothesis testing perspective and found an optimal scaling factor that minimizes the deviation between the empirical and the nominal type I error. Applying their approach to various simulation studies and real examples, the researchers demonstrate that it is more accurate and robust than the state-of-the-art alternatives in detecting differentially expressed genes.

M versus A plots of different rates of DE genes

rna-seq

The left panel and right panel are the MA plots for DE genes at a rate of 0.1 and 0.5, respectively. The blue line is the scale of TMM normalization and the red line is the scale of HTN normalization.

Zhou Y, Wang G, Zhang J, Li H (2017) A Hypothesis Testing Based Method for Normalization and Differential Expression Analysis of RNA-Seq Data. PLoS ONE 12(1): e0169594. [article]

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