ArticleBMC bioinformatics2012
Comparison of co-expression measures: mutual information, correlation, and model based indices.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 224 papers, 1 of them a synthesis that pooled it.
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Who cites it
224 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- Accelerating crop improvement via integration of transcriptome-based network biology and genome editing.Planta · 2025Review
- Dual-approach co-expression analysis framework (D-CAF) enables identification of novel circadian co-regulation from multi-omic timeseries data.BMC bioinformatics · 2025Article
- MulNet: a scalable framework for reconstructing intra- and intercellular signaling networks from bulk and single-cell RNA-seq data.Briefings in bioinformatics · 2025Article
- Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.Nucleic acids research · 2025Article
- ACT2.6: Global Gene Coexpression Network inGenes · 2025Article
- Multi-omics analysis in inclusion body myositis identifies mir-16 responsible for HLA overexpression.Orphanet journal of rare diseases · 2025Article
- Jiawei Duhuo Jisheng Mixture Mitigates Osteoarthritis Progression in Rabbits by Inhibiting Inflammation: A Network Pharmacology and Experimental Approach.Combinatorial chemistry & high throughput screening · 2025Article
- GenPath-PPH: Integrating gene expression and pathway networks via persistent path homology enhances detection of disease-relevant pathways.Computational and structural biotechnology journal · 2025Article
164 more citing papers are in PubMed but not listed here.
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Abstract
backgroundCo-expression measures are often used to define networks among genes. Mutual information (MI) is often used as a generalized correlation measure. It is not clear how much MI adds beyond standard (robust) correlation measures or regression model based association measures. Further, it is important to assess what transformations of these and other co-expression measures lead to biologically meaningful modules (clusters of genes).
resultsWe provide a comprehensive comparison between mutual information and several correlation measures in 8 empirical data sets and in simulations. We also study different approaches for transforming an adjacency matrix, e.g. using the topological overlap measure. Overall, we confirm close relationships between MI and correlation in all data sets which reflects the fact that most gene pairs satisfy linear or monotonic relationships. We discuss rare situations when the two measures disagree. We also compare correlation and MI based approaches when it comes to defining co-expression network modules. We show that a robust measure of correlation (the biweight midcorrelation transformed via the topological overlap transformation) leads to modules that are superior to MI based modules and maximal information coefficient (MIC) based modules in terms of gene ontology enrichment. We present a function that relates correlation to mutual information which can be used to approximate the mutual information from the corresponding correlation coefficient. We propose the use of polynomial or spline regression models as an alternative to MI for capturing non-linear relationships between quantitative variables.
conclusionThe biweight midcorrelation outperforms MI in terms of elucidating gene pairwise relationships. Coupled with the topological overlap matrix transformation, it often leads to more significantly enriched co-expression modules. Spline and polynomial networks form attractive alternatives to MI in case of non-linear relationships. Our results indicate that MI networks can safely be replaced by correlation networks when it comes to measuring co-expression relationships in stationary data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.