Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark.
Methods Mol Biol. 2021;2228:433-451. doi: 10.1007/978-1-0716-1024-4_30.
Data clustering facilitates the identification of biologically relevant molecular features in quantitative proteomics experiments with thousands of measurements over multiple conditions. It finds groups of proteins or peptides with similar quantitative behavior across multiple experimental conditions. This co-regulatory behavior suggests that the proteins of such a group share their functional behavior and thus often can be mapped to the same biological processes and molecular subnetworks.While usual clustering approaches dismiss the variance of the measured proteins, VSClust combines statistical testing with pattern recognition into a common algorithm. Here, we show how to use the VSClust web service on a large proteomics data set and present further tools to assess the quantitative behavior of protein complexes.
数据聚类有助于识别在数千次测量和多个条件下进行的定量蛋白质组学实验中的生物学相关分子特征。它可以找到在多个实验条件下具有相似定量行为的蛋白质或肽的组。这种共同调节行为表明，这样一组的蛋白质共享它们的功能行为，因此通常可以映射到相同的生物过程和分子子网络。虽然通常的聚类方法忽略了所测量的蛋白质的方差，但 VSClust 将统计检验与模式识别结合到一个通用算法中。在这里，我们展示了如何在一个大型蛋白质组学数据集上使用 VSClust 网络服务，并提供了进一步的工具来评估蛋白质复合物的定量行为。
