Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data

Background Constructing gene coexpression networks is a powerful approach for analyzing high-throughput gene expression data towards module identification, gene function prediction, and disease-gene prioritization. While optimal workflows for constructing coexpression networks – including good choices for data pre-processing, normalization, and network transformation – have been developed for microarray-based expression data, such well-tested choices do not exist for RNA-seq data. Almost all studies that compare data processing/normalization methods for RNA-seq focus on the end goal of determining differential gene expression.

Results Here, we present a comprehensive benchmarking and analysis of 30 different workflows, each with a unique set of normalization and network transformation methods, for constructing coexpression networks from RNA-seq datasets. We tested these workflows on both large, homogenous datasets (Genotype-Tissue Expression project) and small, heterogeneous datasets from various labs (submitted to the Sequence Read Archive). We analyzed the workflows in terms of aggregate performance, individual method choices, and the impact of multiple dataset experimental factors. Our results demonstrate that between-sample normalization has the biggest impact, with trimmed mean of M-values or upper quartile normalization producing networks that most accurately recapitulate known tissue-naive and tissue-specific gene functional relationships.

Conclusions Based on this work, we provide concrete recommendations on robust procedures for building an accurate coexpression network from an RNA-seq dataset. In addition, researchers can examine all the results in great detail at https://krishnanlab.github.io/norm_for_RNAseq_coexp to make appropriate choices for coexpression analysis based on the experimental factors of their RNA-seq dataset.