Dissection of Regulatory Networks that Are Altered in Disease via Differential Co-expression
Open accessAbstract
Comparing the gene-expression profiles of sick and healthy individuals can help in understanding disease. Such differential expression analysis is a well-established way to find gene sets whose expression is altered in the disease. Recent approaches to gene-expression analysis go a step further and seek differential co-expression patterns, wherein the level of co-expression of a set of genes differs markedly between disease and control samples. Such patterns can arise from a disease-related change in the regulatory mechanism governing that set of genes, and pinpoint dysfunctional regulatory networks. Here we present DICER, a new method for detecting differentially co-expressed gene sets using a novel probabilistic score for differential correlation. DICER goes beyond standard differential co-expression and detects pairs of modules showing differential co-expression. The expression profiles of genes within each module of the pair are correlated across all samples. The correlation between the two modules, however, differs markedly between the disease and normal samples. We show that DICER outperforms the state of the art in terms of significance and interpretability of the detected gene sets. Moreover, the gene sets discovered by DICER manifest regulation by disease-specific microRNA families. In a case study on Alzheimer's disease, DICER dissected biological processes and protein complexes into functional subunits that are differentially co-expressed, thereby revealing inner structures in disease regulatory networks.
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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 [4]
The Introduction identifies DICER as a differential co-expression method whose meta-modules contain gene sets correlated across all classes. But the Author Summary says DICER’s first step calculates pairwise differential- and consistent-correlation scores, not modules co-expressed across the pooled study samples. This source supports the identification of DICER, but not the claimed order of operations for it. (LLM, conf=0.86)
Experimental — accuracy not yet measuredGenes that are differentially co-expressed between different sample groups are more likely to be regulators, and are therefore likely to explain differences between phenotypes.
“Genes that are differentially co-expressed between different sample groups are more likely to be regulators, and are therefore likely to explain differences between phenotypes [4, 8–10].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [4]
The Introduction (page unavailable) says changes in co-expression between sample classes may indicate changes in regulation, and the Author Summary says the method can predict disease-associated regulators. But the paper describes differentially co-expressed genes as potentially co-regulated; it does not establish that those genes are themselves more likely to be regulators or that they explain phenotype differences. (LLM, conf=0.87)
Experimental — accuracy not yet measuredDICER has been argued to perform better than DiffCoEx and CoXpress based on functional enrichment analysis of differentially expressed modules.
“DICER has been argued to perform better than DiffCoEx and CoXpress [4] based on functional enrichment analysis of differentially expressed modules.”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [4]
The KEGG pathway enrichment analysis reports higher average enrichment factors for DICER than DiffCoEx, supporting that comparison. It does not include CoXpress in the functional-enrichment comparison; the Extent of DC between gene modules section instead reports that CoXpress found no significant clusters of at least 15 genes. (LLM, conf=0.87)
Experimental — accuracy not yet measuredDifferential co-expression analysis identifies genes with varying co-expression partners under different conditions.
“An increasingly used method that goes beyond traditional co-expression networks is differential co-expression analysis [4–7]. This approach identifies genes with varying co-expression partners under different conditions, such as disease states [4, 8–10], tissue types [11] and developmental stages [12], because these genes are more likely to be regulators that underlie phenotypic differences.”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [4]
Genes with varying co-expression partners under different conditions are more likely to be regulators that underlie phenotypic differences.
“An increasingly used method that goes beyond traditional co-expression networks is differential co-expression analysis [4–7]. This approach identifies genes with varying co-expression partners under different conditions, such as disease states [4, 8–10], tissue types [11] and developmental stages [12], because these genes are more likely to be regulators that underlie phenotypic differences.”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [4]
Differential co-expression analysis has been used to identify genes underlying differences between healthy and disease samples or between different tissues, cell types or species.
“Differential co-expression analysis has been used to identify genes underlying differences between healthy and disease samples [4, 8–10] or between different tissues [11], cell types [5] or species [132, 133].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [4]
DICER is tailored to identify module pairs that correlate differently between sample groups.
“By design, DICER is tailored to identify module pairs that correlate differently between sample groups, e.g. modules that form one large interconnected module in one group compared with several smaller modules in another (Figure 3D).”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [4]
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- Authors
- Amar, D., Safer, H., & Shamir, R.
- Affiliations
- Tel Aviv University (IL)
- ORCID
- Amar, David
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-03-07
- Volume
- 9
- Issue
- 3
- Pages
- e1002955
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3591264
- OpenAlex
- W2019163875
- Cited by
- 169
- References
- 66
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 11 · 2023: 18 · 2022: 18 · 2021: 19 · 2020: 25 · 2019: 16 · 2018: 8 · 2017: 6 · 2016: 19 · 2015: 12 · 2014: 8 · 2013: 3
- FWCI
- 6.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Israel Science Foundation (41.11, 802/08)
- Tel Aviv University
- Israeli Centers for Research Excellence (41/11)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- differential co-expression, gene regulatory networks, Dicer, gene modules, microRNA regulation, Alzheimer's disease, gene expression profiling
- MeSH terms
- Algorithms, Communicable Diseases, Humans, Models, Biological, Neoplasms, Inflammatory Bowel Diseases, Cluster Analysis, Computational Biology, Neurodegenerative Diseases, Gene Expression Profiling, Databases, Genetic, MicroRNAs, Gene Regulatory Networks, Protein Interaction Maps
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