A computational approach to generate highly conserved gene co-expression networks with RNA-seq data

Zainab Arshad; John F McDonald

doi:10.1016/j.xpro.2022.101432

A computational approach to generate highly conserved gene co-expression networks with RNA-seq data

STAR Protoc. 2022 Jun 2;3(2):101432. doi: 10.1016/j.xpro.2022.101432. eCollection 2022 Jun 17.

Authors

Zainab Arshad¹, John F McDonald¹

Affiliation

¹ Integrated Cancer Research Center, School of Biological Sciences, Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30619, USA.

Abstract

We describe a consensus approach for network construction based on fully conserved gene-gene interactions from randomly downsampled data subsets for an unbiased differential analysis of gene co-expression networks. The pipeline allows users to identify network nodes lost, conserved, and acquired in cancer as well as interpret the functional significance of these network changes. For proof of concept, the protocol is used to leverage RNA-seq data of tumor samples from TCGA and healthy tissue samples from the GTEx database. For complete details on the use and execution of this protocol, please refer to Arshad and McDonald (2021).

Keywords: Bioinformatics; Cancer; Gene Expression; RNAseq.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Computational Biology* / methods
Gene Expression Profiling* / methods
Gene Regulatory Networks / genetics
RNA-Seq
Sequence Analysis, RNA / methods