ArticleBMC systems biology2010
Gene set analysis exploiting the topology of a pathway.
Article in BMC systems biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Cell line-specific gene network enrichment analysis for interpreting continuous phenotypes.Briefings in bioinformatics · 2026Article
- Pathway-guided architectures for interpretable AI in biological research.Computational and structural biotechnology journal · 2025Review
- bootGSEA: a bootstrap and rank aggregation pipeline for multi-study and multi-omics enrichment analyses.Frontiers in bioinformatics · 2024Article
- A comprehensive survey of the approaches for pathway analysis using multi-omics data integration.Briefings in bioinformatics · 2022Review
- SEMgsa: topology-based pathway enrichment analysis with structural equation models.BMC bioinformatics · 2022Article
- Integrating knowledge and omics to decipher mechanisms via large-scale models of signaling networks.Molecular systems biology · 2022Review
- Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis.Biomolecules · 2022Article
- On the influence of several factors on pathway enrichment analysis.Briefings in bioinformatics · 2022Review
- VarSAn: associating pathways with a set of genomic variants using network analysis.Nucleic acids research · 2021Article
- netgsa: Fast computation and interactive visualization for topology-based pathway enrichment analysis.PLoS computational biology · 2021Article
- Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources.Metabolites · 2020Review
- Gene Set Analysis: Challenges, Opportunities, and Future Research.Frontiers in genetics · 2020Review
- A comparative study of topology-based pathway enrichment analysis methods.BMC bioinformatics · 2019Article
- Identifying significantly impacted pathways: a comprehensive review and assessment.Genome biology · 2019Review
- SourceSet: A graphical model approach to identify primary genes in perturbed biological pathways.PLoS computational biology · 2019Article
- A comparison of mechanistic signaling pathway activity analysis methods.Briefings in bioinformatics · 2019Review
- A probabilistic pathway score (PROPS) for classification with applications to inflammatory bowel disease.Bioinformatics (Oxford, England) · 2018Article
- Article
- A knowledge-based T2-statistic to perform pathway analysis for quantitative proteomic data.PLoS computational biology · 2017Article
- High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes.Oncotarget · 2017Article
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3 authors.
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Abstract
backgroundRecently, a great effort in microarray data analysis is directed towards the study of the so-called gene sets. A gene set is defined by genes that are, somehow, functionally related. For example, genes appearing in a known biological pathway naturally define a gene set. The gene sets are usually identified from a priori biological knowledge. Nowadays, many bioinformatics resources store such kind of knowledge (see, for example, the Kyoto Encyclopedia of Genes and Genomes, among others). Although pathways maps carry important information about the structure of correlation among genes that should not be neglected, the currently available multivariate methods for gene set analysis do not fully exploit it.
resultsWe propose a novel gene set analysis specifically designed for gene sets defined by pathways. Such analysis, based on graphical models, explicitly incorporates the dependence structure among genes highlighted by the topology of pathways. The analysis is designed to be used for overall surveillance of changes in a pathway in different experimental conditions. In fact, under different circumstances, not only the expression of the genes in a pathway, but also the strength of their relations may change. The methods resulting from the proposal allow both to test for variations in the strength of the links, and to properly account for heteroschedasticity in the usual tests for differential expression.
conclusionsThe use of graphical models allows a deeper look at the components of the pathway that can be tested separately and compared marginally. In this way it is possible to test single components of the pathway and highlight only those involved in its deregulation.
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