vissE – Visualising gene–set enrichment analysis results using a network-based approach

Abstract

Gene-set enrichment analysis is a powerful bioinformatics tool to identify the functional processes underlying biological systems. This analysis often used to functionally annotate gene lists derived from a range of workflows including differential expression analysis. Most analyses result in hundreds of significantly enriched gene-sets. Biologists are then tasked with sifting through these lists of gene-sets and extracting relevant knowledge pertaining to their experiment. This process works towards answering the specific hypotheses posed in the experiment, however, will miss the generation of numerous novel hypotheses. To address this issue, I developed a network-based visualisation approach that condenses the numerous gene-sets identified from gene-set enrichment analyses into broader “biological themes”. I exploit the relatedness of gene-sets to cluster related gene-sets into biological themes, which I then automatically annotate using text-mining approaches. In this talk, I will describe vissE, and demonstrate two use cases demonstrating its use in interpreting the results of gene-set enrichment analysis and in exploring molecular phenotypes in single-cell RNA-seq data. A vissE analysis can assist biologists in identifying biological themes in their experiments that they can then use to derive novel hypotheses. Visualisations generated using vissE combine gene-level statistics with condensed gene-set enrichment analysis results thus providing a more holistic view of the biological system being investigated.

Dharmesh D Bhuva

Senior post-doctoral researcher at SAiGENCI

My research interests include cancer systems biology, spatial statistics and computational biology.