ReviewGenome biology2019
Identifying significantly impacted pathways: a comprehensive review and assessment.
Review in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 137 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
137 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Gene expression signatures of response to fluoxetine treatment: systematic review and meta-analyses.Molecular psychiatry · 2025Pooled it
- Causal network analysis of omics data using prior knowledge databases.Briefings in bioinformatics · 2025Pooled it
- Altered methylation pattern in EXOC4 is associated with stroke outcome: an epigenome-wide association study.Clinical epigenetics · 2022Pooled it
- Metabolomic Profiles Associated With Blood Pressure Reduction in Response to the DASH and DASH-Sodium Dietary Interventions.Hypertension (Dallas, Tex. : 1979) · 2023Trial
- ToxCompl completion of the DrugMatrix toxicogenomics database: an integrated resource for hypothesis generation.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- Urinary metabolomic signatures of muscle injury and recovery in elite football players.Metabolomics : Official journal of the Metabolomic Society · 2026Observational
- IAN, an intelligent system for omics data analysis and discovery.Cell reports methods · 2026Article
- Helminth infection induces RELMα-dependent adipose tissue transcriptional reprogramming and protection against diet-induced obesity.bioRxiv : the preprint server for biology · 2026Article
- Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.Briefings in bioinformatics · 2026Article
- Cell line-specific gene network enrichment analysis for interpreting continuous phenotypes.Briefings in bioinformatics · 2026Article
- Metabolomic profiling of Bacillus amyloliquefaciens MU-10 and its antagonistic effects on Cercospora arachidicola.BMC microbiology · 2026Article
- The quantified immune-aging dysregulation index: a large-language model-powered method for annotating and quantifying systems-level dysregulation.Frontiers in artificial intelligence · 2026Article
- A practical guide to functional enrichment analysis.Frontiers in bioinformatics · 2026Article
- Benchmarking multiple gene ontology enrichment tools reveals high biological significance, ranking, and stringency heterogeneity among datasets.Frontiers in bioinformatics · 2026Article
- A Critical Evaluation of Background Gene Omission in Imaging Transcriptomics.Biological psychiatry global open science · 2025Article
- Natural language processing of gene descriptions for overrepresentation analysis with GeneTEA.Genome biology · 2025Article
- Peripheral nerve-targeting and pain-promoting transcriptomic signatures in early Guillain-Barré syndrome.bioRxiv : the preprint server for biology · 2025Article
- Simulated metabolic profiles reveal biases in pathway analysis methods.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- SomaModules: A Pathway Enrichment Approach Tailored to SomaScan Data.Journal of proteome research · 2025Article
- Cluefish: mining the dark matter of transcriptional data series with over-representation analysis enhanced by aggregated biological prior knowledge.NAR genomics and bioinformatics · 2025Article
77 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
backgroundMany high-throughput experiments compare two phenotypes such as disease vs. healthy, with the goal of understanding the underlying biological phenomena characterizing the given phenotype. Because of the importance of this type of analysis, more than 70 pathway analysis methods have been proposed so far. These can be categorized into two main categories: non-topology-based (non-TB) and topology-based (TB). Although some review papers discuss this topic from different aspects, there is no systematic, large-scale assessment of such methods. Furthermore, the majority of the pathway analysis approaches rely on the assumption of uniformity of p values under the null hypothesis, which is often not true.
resultsThis article presents the most comprehensive comparative study on pathway analysis methods available to date. We compare the actual performance of 13 widely used pathway analysis methods in over 1085 analyses. These comparisons were performed using 2601 samples from 75 human disease data sets and 121 samples from 11 knockout mouse data sets. In addition, we investigate the extent to which each method is biased under the null hypothesis. Together, these data and results constitute a reliable benchmark against which future pathway analysis methods could and should be tested.
conclusionOverall, the result shows that no method is perfect. In general, TB methods appear to perform better than non-TB methods. This is somewhat expected since the TB methods take into consideration the structure of the pathway which is meant to describe the underlying phenomena. We also discover that most, if not all, listed approaches are biased and can produce skewed results under the null.
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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.