Pairwise Nonlinear Dependence Analysis of Genomic Data
Applications
2022-11-30 v4 Methodology
Abstract
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.
Keywords
Cite
@article{arxiv.2202.09880,
title = {Pairwise Nonlinear Dependence Analysis of Genomic Data},
author = {Siqi Xiang and Wan Zhang and Siyao Liu and Katherine A. Hoadley and Charles M. Perou and Kai Zhang and J. S. Marron},
journal= {arXiv preprint arXiv:2202.09880},
year = {2022}
}
Comments
21 pages, 2 supplements