Robust Detection of Periodic Patterns in Gene Expression Microarray Data using Topological Signal Analysis

TL;DR

This paper introduces a topological signal analysis method using persistent homology to accurately detect periodic gene expression patterns in microarray data, demonstrating robustness to noise and data irregularities.

Contribution

The paper presents a novel topological approach for identifying cell cycle genes in microarray data, leveraging delay coordinate embeddings and persistent homology.

Findings

Accurately detects periodic genes in noisy data

Robust to missing data and irregular sampling

Validated on yeast gene expression dataset

Abstract

In this paper, we present a new approach for analyzing gene expression data that builds on topological characteristics of time series. Our goal is to identify cell cycle regulated genes in micro array dataset. We construct a point cloud out of time series using delay coordinate embeddings. Persistent homology is utilized to analyse the topology of the point cloud for detection of periodicity. This novel technique is accurate and robust to noise, missing data points and varying sampling intervals. Our experiments using Yeast Saccharomyces cerevisiae dataset substantiate the capabilities of the proposed method.