Abstract
Differential gene expression (DGE) studies often suffer from poor interpretability of their primary results, i.e., thousands of differentially expressed genes. This has led to the introduction of gene set analysis (GSA) methods that aim at identifying interpretable global effects by grouping genes into sets of common context, such as, molecular pathways, biological function or tissue localization. In practice, GSA often results in hundreds of differentially regulated gene sets. Similar to the genes they contain, gene sets are often regulated in a correlative fashion because they share many of their genes or they describe related processes. Using these kind of neighborhood information to construct networks of gene sets allows to identify highly connected sub-networks as well as poorly connected islands or singletons. We show here how topological information and other network features can be used to filter and prioritize gene sets in routine DGE studies. Community detection in combination with automatic labeling and the network representation of gene set clusters further constitute an appealing and intuitive visualization of GSA results. The RICHNET workflow described here does not require human intervention and can thus be conveniently incorporated in automated analysis pipelines.
Highlights
Interpretation of whole-transcriptome differential expression studies is often difficult because the sheer volume of the differentially expressed genes (DEGs) can be overwhelming
One way to handle the vast numbers and to identify the biological consequences of gene expression changes is to associate them with overarching processes involving a whole set of genes, such as Gene Ontology (GO) terms or Kyoto Enzyclopedia of Genes and Genomes (KEGG) pathways
We have presented an automated workflow based on a small number of R packages for prioritization and visualization of gene set analysis results using networks, which we call RICHNET
Summary
1. Kimberly Glass, Harvard Medical School, Boston, USA Harvard T.H. Chan School of Public Health, Harvard University, Boston, USA. 2. Monther Alhamdoosh , CSL Limited, Parkville, Australia The University of Melbourne, Parkville, Australia. Any reports and responses or comments on the article can be found at the end of the article. Author roles: Prummer M: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing. How to cite this article: Prummer M. This revised manuscript is based upon valuable input from the reviewers.
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