DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules
Open accessAbstract
Background: Large microarray datasets have enabled gene regulation to be studied through coexpression analysis. While numerous methods have been developed for identifying differentially expressed genes between two conditions, the field of differential coexpression analysis is still relatively new. More specifically, there is so far no sensitive and untargeted method to identify gene modules (also known as gene sets or clusters) that are differentially coexpressed between two conditions. Here, sensitive and untargeted means that the method should be able to construct de novo modules by grouping genes based on shared, but subtle, differential correlation patterns. Results: We present DiffCoEx, a novel method for identifying correlation pattern changes, which builds on the commonly used Weighted Gene Coexpression Network Analysis (WGCNA) framework for coexpression analysis. We demonstrate its usefulness by identifying biologically relevant, differentially coexpressed modules in a rat cancer dataset. Conclusions: DiffCoEx is a simple and sensitive method to identify gene coexpression differences between multiple conditions.
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The most frequently used programs for differential clustering analysis are WGCNA, DICER and DiffCoEx, all of which first identify modules co-expressed across the full set of study samples.
“The most frequently used programs for differential clustering analysis, which have also been compared with others programs, are WGCNA [54], DICER [4] and DiffCoEx [100], all of which first identify modules co-expressed across the full set of study samples.”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [100]
The paper identifies DiffCoEx as a differential coexpression method, but contradicts the claim about its first step. Step 1 calculates gene correlations within each condition; Steps 2–4 use changes in those correlations to identify modules, rather than first identifying modules coexpressed across the full set of samples. The indexed copy provides section labels but no page numbers. (LLM, conf=0.88)
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Experimental — accuracy not yet measuredDiffCoEx focuses on modules that are differentially co-expressed with the same sets of genes.
“DiffCoEx focuses on modules that are differentially co-expressed with the same sets of genes.”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [100]
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References 25 · Cited by 244
- Authors
- Tesson, B. M., Breitling, R., & Jansen, R. C.
- Affiliations
- University of Groningen (NL)
- University Medical Center Groningen (NL)
- Corresponding author
- Jansen, Ritsert C
- ORCID
- Tesson, Bruno M
- Venue
- BMC Bioinformatics
- Published
- 2010-10-06
- Volume
- 11
- Issue
- 1
- Pages
- 497-497
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC2976757
- OpenAlex
- W2095898243
- Cited by
- 244
- References
- 25
- Citations by year
- 2026: 12 · 2025: 15 · 2024: 15 · 2023: 17 · 2022: 30 · 2021: 23 · 2020: 23 · 2019: 19 · 2018: 14 · 2017: 14 · 2016: 22 · 2015: 14 · 2014: 7 · 2013: 6 · 2012: 6
- FWCI
- 2.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion)
- Research Explorer (The University of Manchester) (submittedVersion, cc-by)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
- Funders
- Norwegian Biodiversity Information Centre
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- gene modules, gene co-expression network, weighted gene co-expression network analysis, gene expression regulation, microarray data, gene clustering
- MeSH terms
- Algorithms, Animals, Pattern Recognition, Automated, Sensitivity and Specificity, Software, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Rats, Gene Regulatory Networks
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