Li Zhaorong, Fuxman Bass Juan I
Bioinformatics Program, Boston University, Boston, MA, United States.
Department of Biology, Boston University, Boston, MA, United States.
Front Bioinform. 2025 Jun 19;5:1604418. doi: 10.3389/fbinf.2025.1604418. eCollection 2025.
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.
从转录组学数据集中提取基因特征对于识别共调控基因集、确定与预后的关联以及发现生物标志物具有重要作用。独立成分分析（ICA）是一种强大的工具，可用于提取此类特征，以揭示复杂数据中的隐藏模式并识别连贯的基因集。ICARus软件包提供了一个强大的流程，可对转录组数据集进行ICA分析。虽然其他软件包使用主参数的一个值（即特征数量）来执行ICA，但ICARus会识别一系列接近最优的参数值，遍历这些值，并评估所识别的特征成分的稳健性和可重复性。为了测试ICARus的性能，我们分析了从不同预后的COVID-19患者和肺腺癌患者获得的转录组数据集。我们识别出了几个与预后、时间模式和细胞类型组成显著相关的可重复基因表达特征。这些特征的基因集富集分析（GSEA）与先前临床研究的结果相符，并揭示了潜在的新生物学机制。带有 vignette 的ICARus可在Github上获取，网址为https://github.com/Zha0rong/ICArus。
