PAIRWISE NONLINEAR DEPENDENCE ANALYSIS OF GENOMIC DATA.

Autor: Xiang S; Department of Statistics and Operations Research, University of North Carolina at Chapel Hill., Zhang W; Department of Statistics and Operations Research, University of North Carolina at Chapel Hill., Liu S; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill.; Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill.; Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill., Hoadley KA; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill.; Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill.; Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill., Perou CM; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill.; Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill.; Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill., Zhang K; Department of Statistics and Operations Research, University of North Carolina at Chapel Hill., Marron JS; Department of Statistics and Operations Research, University of North Carolina at Chapel Hill.; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill.; Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill.
Jazyk: angličtina
Zdroj: The annals of applied statistics [Ann Appl Stat] 2023 Dec; Vol. 17 (4), pp. 2924-2943. Date of Electronic Publication: 2023 Oct 30.
DOI: 10.1214/23-aoas1745
Abstrakt: In The Cancer Genome Atlas (TCGA) data set, there are many interesting nonlinear dependencies between pairs of genes that reveal important relationships and subtypes of cancer. Such genomic data analysis requires a rapid, powerful and interpretable detection process, especially in a high-dimensional environment. We study the nonlinear patterns among the expression of pairs of genes from TCGA using a powerful tool called Binary Expansion Testing. We find many nonlinear patterns, some of which are driven by known cancer subtypes, some of which are novel.
Databáze: MEDLINE