Pairwise Nonlinear Dependence Analysis of Genomic Data

TL;DR

This paper introduces a method for detecting nonlinear dependencies between gene pairs in high-dimensional genomic data, revealing both known and novel cancer-related patterns.

Contribution

It applies Binary Expansion Testing to TCGA data, providing a rapid, interpretable approach to uncover nonlinear gene relationships in cancer research.

Findings

Many nonlinear gene dependencies identified

Some patterns linked to known cancer subtypes

Discovery of novel nonlinear relationships

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.