Integrative clustering methods of multi‐omics data for molecule‐based cancer classifications

One goal of precise oncology is to re‐classify cancer based on molecular features rather than its tissue origin. Integrative clustering of large‐scale multi‐omics data is an important way for molecule‐based cancer classification. The data heterogeneity and the complexity of inter‐omics variations are two major challenges for the integrative clustering analysis. According to the different strategies to deal with these difficulties, we summarized the clustering methods as three major categories: direct integrative clustering, clustering of clusters and regulatory integrative clustering. A few practical considerations on data pre‐processing, post‐clustering analysis and pathway‐based analysis are also discussed.