Efficient Test and Visualization of Multi-Set Intersections
Open accessAbstract
Identification of sets of objects with shared features is a common operation in all disciplines. Analysis of intersections among multiple sets is fundamental for in-depth understanding of their complex relationships. However, so far no method has been developed to assess statistical significance of intersections among three or more sets. Moreover, the state-of-the-art approaches for visualization of multi-set intersections are not scalable. Here, we first developed a theoretical framework for computing the statistical distributions of multi-set intersections based upon combinatorial theory and then accordingly designed a procedure to efficiently calculate the exact probabilities of multi-set intersections. We further developed multiple efficient and scalable techniques to visualize multi-set intersections and the corresponding intersection statistics. We implemented both the theoretical framework and the visualization techniques in a unified R software package, SuperExactTest . We demonstrated the utility of SuperExactTest through an intensive simulation study and a comprehensive analysis of seven independently curated cancer gene sets as well as six disease or trait associated gene sets identified by genome-wide association studies. We expect SuperExactTest developed by this study will have a broad range of applications in scientific data analysis in many disciplines.
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SuperExactTest was used to calculate statistical significance and visualize overlap of differentially expressed genes while comparing each group with APOE3.
“SuperExactTest (Wang et al, 2015) was used to calculate statistical significance (Datasets EV3, EV6) and visualize overlap of differentially expressed genes while comparing each group with APOE3.”
From APOE from astrocytes restores Alzheimer’s Aβ-pathology and DAM-like responses in APOE deficient microglia (2024) · cites this paper as Wang et al, 2015
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- Authors
- Wang, M., Zhao, Y., & Zhang, B.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Zhang, Bin
- Venue
- Scientific Reports
- Published
- 2015-11-25
- Volume
- 5
- Issue
- 1
- Pages
- 16923-16923
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC4658477
- OpenAlex
- W2173168040
- Cited by
- 395
- References
- 6
- Citations by year
- 2026: 33 · 2025: 32 · 2024: 42 · 2023: 33 · 2022: 70 · 2021: 62 · 2020: 44 · 2019: 30 · 2018: 21 · 2017: 20 · 2016: 7 · 2013: 1
- FWCI
- 6.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Institute on Aging (R01AG046170, U01AG046170)
- National Cancer Institute (R01CA163772)
- National Institute of Allergy and Infectious Diseases (U01AI111598)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- statistical significance testing, combinatorics, genome-wide association study, gene set enrichment analysis, R package
- MeSH terms
- Humans, Models, Theoretical, Neoplasms, Phenotype, User-Computer Interface, Internet, Genome-Wide Association Study
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