ReviewEntropy (Basel, Switzerland)2020
Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges.
Review in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
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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.
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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
33 citing papers in PubMed.
- Problems With GO Enrichment as an Endpoint for Ecological Omics Interpretation.Molecular ecology · 2026Article
- Temporal transcriptional dynamics in cutaneous leishmaniasis reveal novel targets for therapeutic interventions in a dermal mouse model.Frontiers in immunology · 2026Article
- Natural language processing of gene descriptions for overrepresentation analysis with GeneTEA.Genome biology · 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
- Artificial Intelligence in Ocular Transcriptomics: Applications of Unsupervised and Supervised Learning.Cells · 2025Review
- Extending gene set variation analysis with a reference dataset to stabilize scores.BMC genomics · 2025Article
- Functional connectivity between the visual and salience networks and autistic social features at school-age.Journal of neurodevelopmental disorders · 2025Article
- Statistical properties of functional connectivity MRI enrichment analysis in school-age autism research.Developmental cognitive neuroscience · 2025Article
- Progress in toxicogenomics to protect human health.Nature reviews. Genetics · 2025Review
- E.PathDash, pathway activation analysis of publicly available pathogen gene expression data.mSystems · 2024Article
- Co-expression gene module analysis in response to attenuated cercaria vaccine reveals a critical role for NK cells in protection against Schistosoma mansoni.Parasites & vectors · 2024Article
- Comparative analysis of single-cell pathway scoring methods and a novel approach.NAR genomics and bioinformatics · 2024Article
- A high-dimensional omnibus test for set-based association analysis.Briefings in bioinformatics · 2024Article
- Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data.Genomics, proteomics & bioinformatics · 2024Article
- A preliminary investigation of epigenome-wide DNA methylation and temperament during infancy.Developmental psychobiology · 2024Article
- Robustness evaluations of pathway activity inference methods on gene expression data.BMC bioinformatics · 2024Article
- NaRnEA: An Information Theoretic Framework for Gene Set Analysis.Entropy (Basel, Switzerland) · 2023Article
- PSF toolkit: an R package for pathway curation and topology-aware analysis.Frontiers in genetics · 2023Article
- Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges.Entropy (Basel, Switzerland) · 2022Review
- massiveGST: A Mann-Whitney-Wilcoxon Gene-Set Test Tool That Gives Meaning to Gene-Set Enrichment Analysis.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Over the last decade, gene set analysis has become the first choice for gaining insights into underlying complex biology of diseases through gene expression and gene association studies. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Although gene set analysis approaches are extensively used in gene expression and genome wide association data analysis, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. In this article, we provide a comprehensive overview, statistical structure and steps of gene set analysis approaches used for microarrays, RNA-sequencing and genome wide association data analysis. Further, we also classify the gene set analysis approaches and tools by the type of genomic study, null hypothesis, sampling model and nature of the test statistic, etc. Rather than reviewing the gene set analysis approaches individually, we provide the generation-wise evolution of such approaches for microarrays, RNA-sequencing and genome wide association studies and discuss their relative merits and limitations. Here, we identify the key biological and statistical challenges in current gene set analysis, which will be addressed by statisticians and biologists collectively in order to develop the next generation of gene set analysis approaches. Further, this study will serve as a catalog and provide guidelines to genome researchers and experimental biologists for choosing the proper gene set analysis approach based on several factors.
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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 Socratic graph.