ArticleBMC genomics2010
Heading down the wrong pathway: on the influence of correlation within gene sets.
Article in BMC genomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
49 citing papers in PubMed.
- Ten common mistakes that could ruin your enrichment analysis.PLoS computational biology · 2026Article
- A workflow for human health hazard evaluation using transcriptomic data and Key Characteristics-based gene sets.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- Direction-aware functional class scoring enrichment analysis of infinium DNA methylation data.Epigenetics · 2024Article
- Addressing erroneous scale assumptions in microbe and gene set enrichment analysis.PLoS computational biology · 2023Article
- FastMix: a versatile data integration pipeline for cell type-specific biomarker inference.Bioinformatics (Oxford, England) · 2022Article
- TWO-SIGMA-G: a new competitive gene set testing framework for scRNA-seq data accounting for inter-gene and cell-cell correlation.Briefings in bioinformatics · 2022Article
- Toward a gold standard for benchmarking gene set enrichment analysis.Briefings in bioinformatics · 2021Article
- Analysis of the immune landscape in virus-induced cancers using a novel integrative mechanism discovery approach.Computational and structural biotechnology journal · 2021Article
- Test-statistic correlation and data-row correlation.Statistics & probability letters · 2020Article
- Application of Transcriptional Gene Modules to Analysis ofG3 (Bethesda, Md.) · 2020Article
- Simultaneous Enrichment Analysis of all Possible Gene-sets: Unifying Self-Contained and Competitive Methods.Briefings in bioinformatics · 2020Review
- Network hub-node prioritization of gene regulation with intra-network association.BMC bioinformatics · 2020Article
- Gene Set Analysis: Challenges, Opportunities, and Future Research.Frontiers in genetics · 2020Review
- Recurrent functional misinterpretation of RNA-seq data caused by sample-specific gene length bias.PLoS biology · 2019Article
- SCIA: A Novel Gene Set Analysis Applicable to Data With Different Characteristics.Frontiers in genetics · 2019Article
- Probabilistic prioritization of candidate pathway association with pathway score.BMC bioinformatics · 2018Article
- The gut microbiota promotes hepatic fatty acid desaturation and elongation in mice.Nature communications · 2018Article
- A strategy for evaluating pathway analysis methods.BMC bioinformatics · 2017Article
- A knowledge-based T2-statistic to perform pathway analysis for quantitative proteomic data.PLoS computational biology · 2017Article
- Extracting the Strongest Signals from Omics Data: Differentially Expressed Pathways and Beyond.Methods in molecular biology (Clifton, N.J.) · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundAnalysis of microarray experiments often involves testing for the overrepresentation of pre-defined sets of genes among lists of genes deemed individually significant. Most popular gene set testing methods assume the independence of genes within each set, an assumption that is seriously violated, as extensive correlation between genes is a well-documented phenomenon.
resultsWe conducted a meta-analysis of over 200 datasets from the Gene Expression Omnibus in order to demonstrate the practical impact of strong gene correlation patterns that are highly consistent across experiments. We show that a common independence assumption-based gene set testing procedure produces very high false positive rates when applied to data sets for which treatment groups have been randomized, and that gene sets with high internal correlation are more likely to be declared significant. A reanalysis of the same datasets using an array resampling approach properly controls false positive rates, leading to more parsimonious and high-confidence gene set findings, which should facilitate pathway-based interpretation of the microarray data.
conclusionsThese findings call into question many of the gene set testing results in the literature and argue strongly for the adoption of resampling based gene set testing criteria in the peer reviewed biomedical literature.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.