Efficient Test and Visualization of Multi-Set Intersections

By Minghui Wang, Yongzhong Zhao, Bin Zhang. Scientific Reports, 2015

372 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/48b9b51a6f12109301bee379a5b21df794e5534a

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Citations in the corpus

Multi-omics single-cell data integration and regulatory inference with graph-linked embedding (2022, methodology): “The significance of marker overlap was determined by the three-way Fisher’s exact test 40 .”