Weighted SAMGSR: combining significance analysis of microarray-gene set reduction algorithm with pathway topology-based weights to select relevant genes

TL;DR

This paper introduces Weighted SAMGSR, a pathway-based gene selection method that incorporates gene connectivity topology to improve the identification of relevant genes in microarray data.

Contribution

The study proposes a novel weighted extension of SAMGSR that integrates gene connectivity information into the feature selection process.

Findings

Weighted SAMGSR outperforms traditional SAMGSR in accuracy and stability.

Gene connectivity enhances the biological relevance of selected genes.

The method is validated on simulated and real-world datasets.

Abstract

Introduction It has been demonstrated that a pathway-based feature selection method which incorporates biological information within pathways into the process of feature selection usually outperform a gene-based feature selection algorithm in terms of predictive accuracy, stability, and biological interpretation. Significance analysis of microarray-gene set reduction algorithm (SAMGSR), an extension to a gene set analysis method with further reduction of the selected pathways to their respective core subsets, can be regarded as a pathway-based feature selection method. Results and Discussion In SAMGSR, whether a gene is selected is mainly determined by its expression difference between the phenotypes, and partially by the number of pathways to which this gene belongs, but ignoring the topology information among pathways. In this study, we propose a weighted version of the SAMGSR algorithm by constructing weights based on the connectivity among genes and then incorporating these weights in the test statistic. Conclusions Using both simulated and real-world data, we evaluate the performance of the proposed SAMGSR extension and demonstrate that gene connectivity is indeed informative for feature selection.