4.5 Article

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

期刊

GENOME BIOLOGY
卷 23, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s13059-021-02568-9

关键词

Gene expression; Data normalization; Network reconstruction

资金

  1. US National Institutes of Health (NIH) [R35 GM128765]
  2. MSU start-up funds

向作者/读者索取更多资源

This study provides a comprehensive benchmarking and analysis of 36 different workflows for constructing coexpression networks from RNA-seq datasets. The results demonstrate that between-sample normalization has the biggest impact, with counts adjusted by size factors producing networks that most accurately recapitulate known gene functional relationships. The findings provide researchers with concrete recommendations for building accurate coexpression networks from RNA-seq datasets.
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 and 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 36 different workflows, each with a unique set of normalization and network transformation methods, for constructing coexpression networks from RNA-seq datasets. We test these workflows on both large, homogenous datasets and small, heterogeneous datasets from various labs. We analyze 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 counts adjusted by size factors producing networks that most accurately recapitulate known tissue-naive and tissue-aware 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 to make appropriate choices for coexpression analysis based on the experimental factors of their RNA-seq dataset.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据