Cheng, CW orcid.org/0000-0002-2873-0828, Beech, DJ orcid.org/0000-0002-7683-9422 and Wheatcroft, SB orcid.org/0000-0002-6741-9012 (2020) Advantages of CEMiTool for gene co-expression analysis of RNA-seq data. Computers in Biology and Medicine, 125. 103975. ISSN 0010-4825
Abstract
Gene co-expression analysis is widely applied to transcriptomics data to associate clusters of genes with biological functions or identify therapeutic targets in diseases. Recently, the emergence of high-throughput technologies for gene expression analyses allows researchers to establish connections through gene co-expression analysis to identify clinical disease markers. However, gene co-expression analysis is complex and may be a daunting task. Here, we evaluate three co-expression analysis packages (WGCNA, CEMiTool, and coseq) using published RNA-seq datasets derived from ischemic cardiomyopathy and chronic obstructive pulmonary disease. Results show that the packages produced consensus co-expression clusters using default parameters. CEMiTool package outperformed the other two packages and required less computational resource and bioinformatics experience. This evaluation provides a basis on which data analysts can select bioinformatics tools for gene co-expression analysis.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier Ltd. All rights reserved. This is an author produced version of an article published in Computers in Biology and Medicine. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | RNA-seq; Co-expression; Ischemic cardiomyopathy; Chronic obstructive pulmonary Disease; WGCNA; CEMiTool; Coseq |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Discovery & Translational Science Dept (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Sep 2020 13:40 |
Last Modified: | 19 Jan 2023 16:48 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.compbiomed.2020.103975 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:165025 |