ArticleBioinformatics (Oxford, England)2009
Statistical methods for gene set co-expression analysis.
Article in Bioinformatics (Oxford, England), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 2 of them syntheses that pooled it.
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Who cites it
91 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- MetaGSCA: A tool for meta-analysis of gene set differential coexpression.PLoS computational biology · 2021Pooled it
- MetaDCN: meta-analysis framework for differential co-expression network detection with an application in breast cancer.Bioinformatics (Oxford, England) · 2017Pooled it
- Statistical Inference of Phenotype-Specific Molecular Mechanisms from Cell Line-Specific Gene Regulatory Networks with Application to Quizartinib Sensitivity.International journal of molecular sciences · 2026Article
- Analyzing Co-expression Networks with Network Skeleton Extraction.Research square · 2026Article
- Uncovering coordinated pathway interactions through gene co-differential expression in yeast.Nucleic acids research · 2026Article
- A powerful framework for differential co-expression analysis of general risk factors.Bioinformatics (Oxford, England) · 2025Article
- Weighted overlapping group lasso for integrating prior network knowledge into gene set analysis.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
- Computational network biology analysis revealed COVID-19 severity markers: Molecular interplay between HLA-II with CIITA.PloS one · 2025Article
- Dynamic relationships among pathways producing hydrocarbons and fatty acids of maize silk cuticular waxes.Plant physiology · 2024Article
- Multivariate differential association analysis.Stat (International Statistical Institute) · 2024Article
- ALKBH5 modulates macrophages polarization in tumor microenvironment of ovarian cancer.Journal of ovarian research · 2024Article
- Identification of gene regulatory networks affected across drug-resistant epilepsies.Nature communications · 2024Article
- Patient-specific analysis of co-expression to measure biological network rewiring in individuals.Life science alliance · 2024Article
- Limitation of permutation-based differential correlation analysis.Genetic epidemiology · 2023Article
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- Early Detection of Pancreatic Cancers Using Liquid Biopsies and Hierarchical Decision Structure.IEEE journal of translational engineering in health and medicine · 2022Article
- Network Analysis of Microarray Data.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Nonalcoholic fatty liver disease, circulating ketone bodies and all-cause mortality in a general population-based cohort.European journal of clinical investigation · 2021Article
31 more citing papers are in PubMed but not listed here.
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2 authors.
Funding
Abstract
motivationThe power of a microarray experiment derives from the identification of genes differentially regulated across biological conditions. To date, differential regulation is most often taken to mean differential expression, and a number of useful methods for identifying differentially expressed (DE) genes or gene sets are available. However, such methods are not able to identify many relevant classes of differentially regulated genes. One important example concerns differentially co-expressed (DC) genes.
resultsWe propose an approach, gene set co-expression analysis (GSCA), to identify DC gene sets. The GSCA approach provides a false discovery rate controlled list of interesting gene sets, does not require that genes be highly correlated in at least one biological condition and is readily applied to data from individual or multiple experiments, as we demonstrate using data from studies of lung cancer and diabetes. AVAILABILITY: The GSCA approach is implemented in R and available at www.biostat.wisc.edu/ approximately kendzior/GSCA/. CONTACT: kendzior@biostat.wisc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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