ArticleBioinformatics (Oxford, England)2014
Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 1 of them a synthesis that pooled it.
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Who cites it
61 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
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- GeDi: simplifying gene set distances for enhanced omics interpretation in R/Bioconductor.BMC bioinformatics · 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
- Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease.Frontiers in neuroscience · 2025Article
- Construction of a pathway-level model for preeclampsia based on gene expression data.Hypertension research : official journal of the Japanese Society of Hypertension · 2024Article
- Ant colony optimization for the identification of dysregulated gene subnetworks from expression data.BMC bioinformatics · 2024Article
- Multivariate differential association analysis.Stat (International Statistical Institute) · 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
- Illuminating (HTLV-1)-induced adult T-cell leukemia/lymphoma transcriptomic signature: A systems virology approach.Virus research · 2023Article
- Delving into gene-set multiplex networks facilitated by a k-nearest neighbor-based measure of similarity.Computational and structural biotechnology journal · 2023Article
- Gene network inference from single-cell omics data and domain knowledge for constructing COVID-19-specificFrontiers in genetics · 2023Article
- Article
- Review
- Network Analysis of Microarray Data.Methods in molecular biology (Clifton, N.J.) · 2022Article
- SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data.Journal of statistical software · 2021Article
- Pathway testing for longitudinal metabolomics.Statistics in medicine · 2021Article
- Article
1 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
motivationTo date, gene set analysis approaches primarily focus on identifying differentially expressed gene sets (pathways). Methods for identifying differentially coexpressed pathways also exist but are mostly based on aggregated pairwise correlations or other pairwise measures of coexpression. Instead, we propose Gene Sets Net Correlations Analysis (GSNCA), a multivariate differential coexpression test that accounts for the complete correlation structure between genes.
resultsIn GSNCA, weight factors are assigned to genes in proportion to the genes' cross-correlations (intergene correlations). The problem of finding the weight vectors is formulated as an eigenvector problem with a unique solution. GSNCA tests the null hypothesis that for a gene set there is no difference in the weight vectors of the genes between two conditions. In simulation studies and the analyses of experimental data, we demonstrate that GSNCA captures changes in the structure of genes' cross-correlations rather than differences in the averaged pairwise correlations. Thus, GSNCA infers differences in coexpression networks, however, bypassing method-dependent steps of network inference. As an additional result from GSNCA, we define hub genes as genes with the largest weights and show that these genes correspond frequently to major and specific pathway regulators, as well as to genes that are most affected by the biological difference between two conditions. In summary, GSNCA is a new approach for the analysis of differentially coexpressed pathways that also evaluates the importance of the genes in the pathways, thus providing unique information that may result in the generation of novel biological hypotheses. AVAILABILITY AND IMPLEMENTATION: Implementation of the GSNCA test in R is available upon request from the authors.
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