Department of Physiology, University of Alberta, Edmonton, AB T6G 2H7, Canada; Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, AB T6G 2S2, Canada.
Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.
STAR Protoc. 2024 Jun 21;5(2):103041. doi: 10.1016/j.xpro.2024.103041. Epub 2024 Apr 27.
Here, we present a workflow for analyzing multi-omics data of plasma samples in patients with post-COVID condition (PCC). Applicable to various diseases, we outline steps for data preprocessing and integrating diverse assay datasets. Then, we detail statistical analysis to unveil plasma profile changes and identify biomarker-clinical variable associations. The last two steps discuss machine learning techniques for unsupervised clustering of patients based on their inherent molecular similarities and feature selection to identify predictive biomarkers. For complete details on the use and execution of this protocol, please refer to Wang et al..
在这里，我们提出了一种分析新冠后(post-COVID)患者血浆样本多组学数据的工作流程。该方法适用于各种疾病，概述了数据预处理和整合不同检测数据集的步骤。然后，我们详细介绍了统计分析，以揭示血浆谱变化，并确定生物标志物与临床变量的关联。最后两个步骤讨论了基于患者内在分子相似性的无监督聚类和特征选择的机器学习技术，以识别预测生物标志物。有关本方案使用和执行的详细信息，请参考 Wang 等人的研究。
