pyPAGE: A framework for Addressing biases in gene-set enrichment analysis - A case study on Alzheimer's disease

Artemy Bakulin, Noam B. Teyssier, Martin Kampmann, Matvei Khoroshkin, Hani Goodarzi

Abstract: Author summary Biological regulation is governed by a complex network of interactions involving transcription factors, RNA-binding proteins, and microRNAs. To reveal the regulatory programs underlying gene expression modulations, researchers often take advantage of gene-set enrichment analysis, an approach that studies concerted changes in a group of genes rather than observing genes in isolation. Previously, we developed a tool called iPAGE to facilitate this analysis. However, both iPAGE and other similar tools implicitly assume that different genes have uniform gene-set membership. Our recent observations challenge this assumption, revealing that some genes form regulatory networks far more frequently than others. These technical biases and redundancies in gene-set annotations complicate the accurate inference of true regulatory relationships in specific contexts. To overcome these limitations, we introduced pyPAGE, an enhanced method that extends our information-theoretic framework by incorporating conditional mutual information to account for specific biases and artifacts. We applied pyPAGE to Alzheimer’s disease, a neurodegenerative disorder marked by its complexity and characterized by progressive cognitive decline, memory loss, and behavioral changes. Pathological processes of Alzheimer’s disease involve various cell types and diverse molecular mechanisms, which pose significant challenges for study. In our study we took advantage of our newly developed framework pyPAGE, performing analysis of gene expression changes at both tissue and cell type levels. This way we were able to describe regulation patterns of known regulators of Alzheimer’s as well as several new ones. Additionally, we performed a cohort study of the association between the enrichment of the identified regulons and survival prognosis for patients, showing that increased activity of a subset of RBPs is positively associated with a lifespan. In total the results highlight the utility of pyPAGE and provide a valuable set of biomarkers for Alzheimer’s disease.

External IDs:dblp:journals/ploscb/BakulinTKKG24