Finding disease specific alterations in the co-expression of genes
Abstract
Motivation: Standard analysis routines for microarray data aim at differentially expressed genes. In this paper, we address the complementary problem of detecting sets of differentially co-expressed genes in two phenotypically distinct sets of expression profiles. Results: We introduce a score for differential co-expression and suggest a computationally efficient algorithm for finding high scoring sets of genes. The use of our novel method is demonstrated in the context of simulations and on real expression data from a clinical study.
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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 [9]
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.
“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 [9]
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 [9]
Differentially connected genes appear to play a regulatory part in the difference in the phenotype observed between two groups.
“These genes appear to play a regulatory part in the difference in the phenotype observed between two groups (Figure 3D) [8–10].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [9]
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Gene co-expression analysis for functional classification and gene–disease predictions
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- Authors
- Kostka, D., & Spang, R.
- Affiliations
- Max Planck Institute for Molecular Genetics (DE)
- Corresponding author
- Kostka, Dennis
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2004-08-04
- Volume
- 20
- Issue
- suppl_1
- Pages
- i194-i199
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2121195642
- Cited by
- 148
- References
- 13
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 2 · 2022: 6 · 2021: 7 · 2020: 11 · 2019: 2 · 2018: 12 · 2017: 8 · 2016: 9 · 2015: 14 · 2014: 7 · 2013: 10 · 2012: 6
- FWCI
- 1.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- University of Regensburg Publication Server (University of Regensburg) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- 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
- differential co-expression, gene co-expression, microarray data, gene expression profiles
- MeSH terms
- Algorithms, Diagnosis, Computer-Assisted, Multigene Family, Humans, Leukemia, Neoplasm Proteins, Sensitivity and Specificity, Biomarkers, Tumor, Reproducibility of Results, Gene Expression Profiling
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