PAIRWISE NONLINEAR DEPENDENCE ANALYSIS OF GENOMIC DATA

• 成果类型: Article
• 署名作者: Xiang, Siqi; Zhang, Wan; Liu, Siyao; Hoadley, Katherine A.; Perou, Charles M.; Zhang, Kai; Marron, J. S.
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1745
• 发表日期: 2023
• 页码: 2924-2943
• 关键词: molecular portraits; breast

摘要

In The Cancer Genome Atlas (TCGA) data set, there are many inter-esting 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 expres-sion of pairs of genes from TCGA using a powerful tool called binary expan-sion testing. We find many nonlinear patterns, some of which are driven by known cancer subtypes, some of which are novel.