A network embedding based method for partial multi-omics integration in cancer subtyping.

Integrative analysis of multiple omics offers the opportunity to uncover coordinated cellular processes acting across different omics layers. The ever-increasing of multi-omics data provides us a comprehensive insight into cancer subtyping. Many multi-omics integrative methods have been developed, but few of them can deal with partial datasets in which some samples only have data for a subset of the omics. In this study, we propose a partial multi-omics integrative method, MSNE (Multiple Similarity Network Embedding), for cancer subtyping. MSNE integrates the multi-omics information by embedding the neighbor relations of samples defined by the random walk on multiple similarity networks. We compared MSNE with five existing multi-omics integrative methods on twelve datasets in both full and partial scenarios. MSNE achieved the best result on pan-cancer and image datasets. Furthermore, on ten cancer subtyping datasets, MSNE got the most enriched clinical parameters and comparable log-rank test P-values in survival analysis. In conclusion, MSNE is an effective and efficient integrative method for multi-omics data and, especially, has a strong power on partial datasets.