Choice of library size normalization and statistical methods for differential gene expression analysis in balanced two-group comparisons for RNA-seq studies

Abstract

Background: High-throughput RNA sequencing (RNA-seq) has evolved as an important analytical tool in molecular biology. Although the utility and importance of this technique have grown, uncertainties regarding the proper analysis of RNA-seq data remain. Of primary concern, there is no consensus regarding which normalization and statistical methods are the most appropriate for analyzing this data. The lack of standardized analytical methods leads to uncertainties in data interpretation and study reproducibility, especially with studies reporting high false discovery rates. In this study, we compared a recently developed normalization method, UQ-pgQ2, with three of the most frequently used alternatives including RLE (relative log estimate), TMM (Trimmed-mean M values) and UQ (upper quartile normalization) in the analysis of RNA-seq data. We evaluated the performance of these methods for gene-level differential expression analysis by considering the factors, including: 1) normalization combined with the choice of a Wald test from DESeq2 and an exact test/QL (Quasi-likelihood) F-Test from edgeR; 2) sample sizes in two balanced two-group comparisons; and 3) sequencing read depths. Results: Using the MAQC RNA-seq datasets with small sample replicates, we found that UQ-pgQ2 normalization combined with an exact test can achieve better performance in term of power and specificity in differential gene expression analysis. However, using an intra-group analysis of false positives from real and simulated data, we found that a Wald test performs better than an exact test when the number of sample replicates is large and that a QL Ftest performs the best given sample sizes of 5, 10 and 15 for any normalization. The RLE, TMM and UQ methods performed similarly given a desired sample size. Conclusion: We found the UQ-pgQ2 method combined with an exact test/QL F-test is the best choice in order to control false positives when the sample size is small. When the sample size is large, UQ-pgQ2 with a QL F-test is a better choice for the type I error control in an intra-group analysis. We observed read depths have a minimal impact for differential gene expression analysis based on the simulated data.

High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

“…All DE analyses were done with R software (version 3.5.3) and the edgeR package [24] (version 3.22.5). Trimmed-mean M values (TMM) normalization was performed to normalize the counts among the different samples [37][38][39][40]. As high dispersion of low counts interfered with some of the statistical approximations used in edgeR, genes with low counts were filtered out using the filterByExpr function as recommended in the user's guide.…”

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“…However, most studies follow cohort analysis using standard statistical algorithms to determine DEGs, where various normalization methods followed by negative binomial distributions or Poisson are utilized to model the gene count data. Cutoff score based on P-value generated by statistical modeling is then applied along with expression change threshold [ 124 , 125 ]. This method of analysis has been successful in different ways, as they could identify biomarkers and prognostic markers and determine which genes are usually overexpressed or downregulated in certain cancer types [ 126 ].…”

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