{"title":"Protocol for analyzing functional gene module perturbation during the progression of diseases using a single-cell Bayesian biclustering framework.","authors":"Kunyue Wang, Yuqiao Gong, Zixin Yan, Zhiyuan Dang, Junhao Wang, Maoying Wu, Yue Zhang","doi":"10.1016/j.xpro.2024.103349","DOIUrl":null,"url":null,"abstract":"<p><p>The pathogenesis of complex diseases involves intricate gene regulation across cell types, necessitating a comprehensive analysis approach. Here, we present a protocol for analyzing functional gene module (FGM) perturbation during the progression of diseases using a single-cell Bayesian biclustering (scBC) framework. We describe steps for setting up the scBC workspace, preparing and exploring input data, training the model, and reconstructing the data matrix. We then detail procedures for Bayesian biclustering, exploring biclustering results, and uncovering pathway perturbations. For complete details on the use and execution of this protocol, please refer to Gong et al.<sup>1</sup>.</p>","PeriodicalId":34214,"journal":{"name":"STAR Protocols","volume":"5 4","pages":"103349"},"PeriodicalIF":1.3000,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11472622/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"STAR Protocols","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.xpro.2024.103349","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/30 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
The pathogenesis of complex diseases involves intricate gene regulation across cell types, necessitating a comprehensive analysis approach. Here, we present a protocol for analyzing functional gene module (FGM) perturbation during the progression of diseases using a single-cell Bayesian biclustering (scBC) framework. We describe steps for setting up the scBC workspace, preparing and exploring input data, training the model, and reconstructing the data matrix. We then detail procedures for Bayesian biclustering, exploring biclustering results, and uncovering pathway perturbations. For complete details on the use and execution of this protocol, please refer to Gong et al.1.
复杂疾病的发病机制涉及跨细胞类型的复杂基因调控,因此需要一种综合分析方法。在这里,我们介绍了一种利用单细胞贝叶斯双聚类(scBC)框架分析疾病进展过程中功能基因模块(FGM)扰动的方案。我们介绍了设置 scBC 工作区、准备和探索输入数据、训练模型和重建数据矩阵的步骤。然后,我们详细介绍了贝叶斯双聚类、探索双聚类结果和发现路径扰动的程序。有关本方案使用和执行的完整细节,请参阅 Gong 等人的文章1。