# ICARus: a pipeline to extract robust gene expression signatures from transcriptome datasets

## TL;DR

ICARus is a pipeline that extracts reliable gene expression signatures from transcriptome data to identify patterns linked to prognosis and biological mechanisms.

## Contribution

ICARus introduces a robust ICA pipeline that identifies multiple near-optimal parameter values and assesses signature reproducibility.

## Key findings

- ICARus identified reproducible gene signatures associated with prognosis in COVID-19 and lung adenocarcinoma.
- Gene Set Enrichment Analysis (GSEA) confirmed clinical relevance and revealed new biological insights.
- The pipeline outperforms existing methods by evaluating a range of parameter values for robustness.

## Abstract

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.