BRIEF RESEARCH REPORT article
Front. Bioinform.
Sec. Genomic Analysis
Volume 5 - 2025 | doi: 10.3389/fbinf.2025.1604418
ICARus: a pipeline to extract robust gene expression signatures from transcriptome datasets
Provisionally accepted- Boston University, Boston, Massachusetts, United States
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Gene signature extraction from transcriptomics datasets has been instrumental to identify sets of co-regulated genes, identify associations with prognosis, and for biomarker discovery. Independent component analysis (ICA) is a powerful tool to extract such signatures to uncover hidden patterns in complex data and identify coherent gene sets. The ICARus package offers a robust pipeline to perform ICA on transcriptome datasets. While other packages perform ICA using one value of the main parameter (i.e., the number of signatures), ICARus identifies a range of near-optimal parameter values, iterates through these values, and assesses the robustness and reproducibility of the signature components identified. To test the performance of ICARus, we analyzed transcriptome datasets obtained from COVID-19 patients with different outcomes and from lung adenocarcinoma. We identified several reproducible gene expression signatures significantly associated with prognosis, temporal patterns, and cell type composition. The GSEA of these signatures matched findings from previous clinical studies and revealed potentially new biological mechanisms.ICARus with a vignette is available on Github https://github.com/Zha0rong/ICArus.
Keywords: Independent Component Analysis, Transcriptomics, signatures, machine learning, robustness
Received: 01 Apr 2025; Accepted: 05 Jun 2025.
Copyright: © 2025 Li and Fuxman Bass. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Juan Ignacio Fuxman Bass, Boston University, Boston, 02215, Massachusetts, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.