ReviewBriefings in bioinformatics2020
Simultaneous Enrichment Analysis of all Possible Gene-sets: Unifying Self-Contained and Competitive Methods.
Review in Briefings in bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
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
15 citing papers in PubMed.
- CD36 enhances sensitivity of triple negative breast cancer cells to palmitate-induced ferroptosis.Cell death & disease · 2026Article
- Multiple Testing of Mix-and-Match Feature Sets in Multi-Omics.Statistics in medicine · 2026Article
- Alterations in Mitochondrial Function in Pulmonary Vascular Diseases.Antioxidants & redox signaling · 2025Article
- A comprehensive framework for trans-ancestry pathway analysis using GWAS summary data from diverse populations.PLoS genetics · 2024Article
- Combining Partial True Discovery Guarantee Procedures.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Generalized reporter score-based enrichment analysis for omics data.Briefings in bioinformatics · 2024Article
- Long COVID manifests with T cell dysregulation, inflammation and an uncoordinated adaptive immune response to SARS-CoV-2.Nature immunology · 2024Article
- GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership.Genome biology · 2023Article
- GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership.bioRxiv : the preprint server for biology · 2023Article
- Integrative pathway and network analysis provide insights on flooding-tolerance genes in soybean.Scientific reports · 2023Article
- Article
- Datasets for gene expression profiles of head and neck squamous cell carcinoma and lung cancer treated or not by PD1/PD-L1 inhibitors.Data in brief · 2022Article
- Toward a gold standard for benchmarking gene set enrichment analysis.Briefings in bioinformatics · 2021Article
- WikiPathways: connecting communities.Nucleic acids research · 2021Article
- Charting host-microbe co-metabolism in skin aging and application to metagenomics data.PloS one · 2021Article
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
No grant is acknowledged in the PubMed record.
Abstract
Studying sets of genomic features is increasingly popular in genomics, proteomics and metabolomics since analyzing at set level not only creates a natural connection to biological knowledge but also offers more statistical power. Currently, there are two gene-set testing approaches, self-contained and competitive, both of which have their advantages and disadvantages, but neither offers the final solution. We introduce simultaneous enrichment analysis (SEA), a new approach for analysis of feature sets in genomics and other omics based on a new unified null hypothesis, which includes the self-contained and competitive null hypotheses as special cases. We employ closed testing using Simes tests to test this new hypothesis. For every feature set, the proportion of active features is estimated, and a confidence bound is provided. Also, for every unified null hypotheses, a $P$-value is calculated, which is adjusted for family-wise error rate. SEA does not need to assume that the features are independent. Moreover, users are allowed to choose the feature set(s) of interest after observing the data. We develop a novel pipeline and apply it on RNA-seq data of dystrophin-deficient mdx mice, showcasing the flexibility of the method. Finally, the power properties of the method are evaluated through simulation studies.
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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.