ArticleBioinformatics (Oxford, England)2019
Differential coexpression in human tissues and the confounding effect of mean expression levels.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- MetaGSCA: A tool for meta-analysis of gene set differential coexpression.PLoS computational biology · 2021Pooled it
- Evaluation of analysis modes for RNA coexpression in single-cell and bulk tissue.bioRxiv : the preprint server for biology · 2026Article
- Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics.PLoS computational biology · 2025Article
- Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review.Computational and structural biotechnology journal · 2024Review
- Co-Expression Analysis of Airway Epithelial Transcriptome in Asthma Patients with Eosinophilic vs. Non-Eosinophilic Airway Infiltration.International journal of molecular sciences · 2023Article
- A Bayesian approach to differential edges with probabilistic interactions: applications in association and classification.Bioinformatics advances · 2023Article
- Network-based approaches for modeling disease regulation and progression.Computational and structural biotechnology journal · 2023Review
- Addressing the mean-correlation relationship in co-expression analysis.PLoS computational biology · 2022Article
- Re-wiring and gene expression changes of AC025034.1 and ATP2B1 play complex roles in early-to-late breast cancer progression.BMC genomic data · 2022Article
- Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data.Genome biology · 2022Article
- Conditional transcriptional relationships may serve as cancer prognostic markers.BMC medical genomics · 2021Article
- Regulatory Network of PD1 Signaling Is Associated with Prognosis in Glioblastoma Multiforme.Cancer research · 2021Article
- Ras isoform-specific expression, chromatin accessibility, and signaling.Biophysical reviews · 2021Review
- Article
- Evolutionary conservation and divergence of the human brain transcriptome.Genome biology · 2021Article
- Review
- Expression correlation attenuates within and between key signaling pathways in chronic kidney disease.BMC medical genomics · 2020Article
- Untangling the effects of cellular composition on coexpression analysis.Genome research · 2020Article
- Whole transcriptomic network analysis using Co-expression Differential Network Analysis (CoDiNA).PloS one · 2020Article
- Network Medicine in the Age of Biomedical Big Data.Frontiers in genetics · 2019Review
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2 authors.
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Abstract
Motivation: Differential coexpression-the alteration of gene coexpression patterns observed in different biological conditions-has been proposed to be a mechanism for revealing rewiring of transcription regulatory networks. Despite wide use of methods for differential coexpression analysis, the phenomenon has not been well-studied. In particular, in many applications, differential coexpression is confounded with differential expression, that is, changes in average levels of expression across conditions. This confounding, despite affecting the interpretation of the differential coexpression, has rarely been studied. Results: We constructed high-quality coexpression networks for five human tissues and identified coexpression links (gene pairs) that were specific to each tissue. Between 3 and 32% of coexpression links were tissue-specific (differentially coexpressed) and this specificity is reproducible in an external dataset. However, we show that up to 75% of the observed differential coexpression is substantially explained by average expression levels of the genes. 'Pure' differential coexpression independent from differential expression is a minority and is less reproducible in external datasets. We also investigated the functional relevance of pure differential coexpression. Our conclusion is that to a large extent, differential coexpression is more parsimoniously explained by changes in average expression levels and pure links have little impact on network-based functional analysis. Supplementary information: Supplementary data are available at Bioinformatics online.
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