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A normalization method based on variance and median adjustment for massive mRNA polyadenylation data

Guoli Ji, Ying Wang, MingchenWu, Yangzi Zhang, Xiaohui Wu

This paper proposed a normalizationmethod based on minimumvariance and median adjustment (MVM), and then made a comprehensive comparison of three normalization methods including DESeq, TMMand MVM. In this study, the MVM method was evaluated using polyadenylation [poly(A)] data and gene expression data fromArabidopsis by ways of empirical statistical criterias of mean square error (MSE) and Kolmogorov-Smirnov (K-S) statistic. Experimental results demonstrated the high performance ofMVMmethod in that it could accurately remove the systematic bias and make the distributions of normalized data stable.