Genomics Data Analysis via Spectral Shape and Topology

TL;DR

This paper introduces a novel workflow combining Mapper and differential gene expression analysis to reveal distinct subgroups in tumor RNA-seq data, uncovering new insights into lung cancer pathways.

Contribution

The paper develops a new approach integrating Mapper with statistical inference tools to analyze high-dimensional genomic data more effectively than existing methods.

Findings

Successfully separated tumor and healthy subjects using Mapper-based graphical structures.

Identified two distinct gene regulation pathways in tumor subgroups.

Demonstrated the superiority of Mapper over t-SNE in revealing tumor heterogeneity.

Abstract

Mapper, a topological algorithm, is frequently used as an exploratory tool to build a graphical representation of data. This representation can help to gain a better understanding of the intrinsic shape of high-dimensional genomic data and to retain information that may be lost using standard dimension-reduction algorithms. We propose a novel workflow to process and analyze RNA-seq data from tumor and healthy subjects integrating Mapper and differential gene expression. Precisely, we show that a Gaussian mixture approximation method can be used to produce graphical structures that successfully separate tumor and healthy subjects, and produce two subgroups of tumor subjects. A further analysis using DESeq2, a popular tool for the detection of differentially expressed genes, shows that these two subgroups of tumor cells bear two distinct gene regulations, suggesting two discrete paths for forming lung cancer, which could not be highlighted by other popular clustering methods, including t-SNE. Although Mapper shows promise in analyzing high-dimensional data, building tools to statistically analyze Mapper graphical structures is limited in the existing literature. In this paper, we develop a scoring method using heat kernel signatures that provides an empirical setting for statistical inferences such as hypothesis testing, sensitivity analysis, and correlation analysis.