ArticleGenome biology2019
Differential co-expression-based detection of conditional relationships in transcriptional data: comparative analysis and application to breast cancer.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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46 citing papers in PubMed.
- Efficient differential latent network analysis: applications to colon cancer.BMC medical genomics · 2026Article
- Sexual Dimorphism in Clinical Manifestations of Knee Osteoarthritis.Medicine and science in sports and exercise · 2026Article
- ceQTL: a co-expression QTL model to detect a variant that affects transcription factor binding and its target regulation.Briefings in bioinformatics · 2026Article
- Subgroup Analysis of Differential Networks with Latent Variables.Statistics and computing · 2025Article
- The Age-dependent Influence of the Infrapatellar Fat Pad on Chondrocyte Extracellular Matrix Production.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- DRaCOon: a novel algorithm for pathway-level differential co-expression analysis in transcriptomics.BMC bioinformatics · 2025Article
- GSNCASCR: An R Package to Identify Differentially Co-Expressed Curated Gene Sets with Single-Cell RNA-Seq Data.International journal of molecular sciences · 2025Article
- Evolutionary divergence between homologous X-Y chromosome genes shapes sex-biased biology.Nature ecology & evolution · 2025Article
- Interpretable AI for inference of causal molecular relationships from omics data.Science advances · 2025Article
- Systematic analysis of the transcriptional landscape of melanoma reveals drug-target expression plasticity.Briefings in functional genomics · 2025Article
- Transcriptome Analysis Reveals the lncRNA-mRNA Co-expression Network Regulating the Aestivation of Sea Cucumber.Marine biotechnology (New York, N.Y.) · 2024Article
- A bootstrap model comparison test for identifying genes with context-specific patterns of genetic regulation.The annals of applied statistics · 2024Article
- Multivariate differential association analysis.Stat (International Statistical Institute) · 2024Article
- Biologically informed NeuralODEs for genome-wide regulatory dynamics.Genome biology · 2024Article
- PMHeliyon · 2024Article
- Patient-specific analysis of co-expression to measure biological network rewiring in individuals.Life science alliance · 2024Article
- Inference of differential gene regulatory networks using boosted differential trees.Bioinformatics advances · 2024Article
- Limitation of permutation-based differential correlation analysis.Genetic epidemiology · 2023Article
- A BOOTSTRAP MODEL COMPARISON TEST FOR IDENTIFYING GENES WITH CONTEXT-SPECIFIC PATTERNS OF GENETIC REGULATION.bioRxiv : the preprint server for biology · 2023Article
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
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4 authors.
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Abstract
backgroundElucidation of regulatory networks, including identification of regulatory mechanisms specific to a given biological context, is a key aim in systems biology. This has motivated the move from co-expression to differential co-expression analysis and numerous methods have been developed subsequently to address this task; however, evaluation of methods and interpretation of the resulting networks has been hindered by the lack of known context-specific regulatory interactions.
resultsIn this study, we develop a simulator based on dynamical systems modelling capable of simulating differential co-expression patterns. With the simulator and an evaluation framework, we benchmark and characterise the performance of inference methods. Defining three different levels of "true" networks for each simulation, we show that accurate inference of causation is difficult for all methods, compared to inference of associations. We show that a z-score-based method has the best general performance. Further, analysis of simulation parameters reveals five network and simulation properties that explained the performance of methods. The evaluation framework and inference methods used in this study are available in the dcanr R/Bioconductor package.
conclusionsOur analysis of networks inferred from simulated data show that hub nodes are more likely to be differentially regulated targets than transcription factors. Based on this observation, we propose an interpretation of the inferred differential network that can reconstruct a putative causal network.
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