MSNE A network embedding based application 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. We developed a partial multi-omics integrative application tool, 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. MSNE is an effective and efficient integrative tool for multi-omics data and, especially, has strong power on partial datasets.