Feature selection for longitudinal microarray data by adapting a pathway analysis method

TL;DR

This paper adapts a gene set analysis method, SAMGSR, for feature selection in longitudinal microarray data, demonstrating its effectiveness in identifying relevant genes and producing parsimonious models.

Contribution

It introduces simple and two-level SAMGSR extensions for feature selection, bridging gene set analysis and feature selection in longitudinal microarray studies.

Findings

Both SAMGSR extensions perform well on real data.

They effectively identify true relevant genes in simulated data.

The models are parsimonious when relevant genes are not highly correlated with irrelevant ones.

Abstract

Introduction: Feature selection and gene set analysis are of increasing interest in bioinformatics. While these two approaches have been developed for different purposes, we describe how some gene set analysis methods can be used to conduct feature selection. Here we adapt the gene set analysis method, significance analysis of microarray gene set reduction (SAMGSR), for feature selection, and propose two extensions-simple SAMGSR and two-level SAMGSR to identify relevant features for longitudinal microarray data. Results and Discussion: When applied to a real-world application, both simple and two-level SAMGSR work comparably well. Using simulated data, we further demonstrate that both SAMGSR extensions have the ability to identify the true relevant genes. If the relevant genes are not highly correlated with the irrelevant ones, the final models given by the two SAMGSR extensions are parsimonious as well. Conclusions: By adapting SAMGSR for feature selection and applying the proposed algorithms on a longitudinal gene expression dataset, we demonstrate that a gene set analysis method can be used for the purpose of feature selection. We believe this work paves the way for more research to bridge feature selection and gene set analysis with the development of novel algorithms.