Cancer Subtype Identification based on Multi-view Subspace Clustering with Adaptive Local Structure Learning

Identifying cancer subtypes is of key significance for the application of personalized medicine. It is devoted to using the unsupervised clustering methods to stratify cancer patients into diverse subgroups and provide specific treatments accordingly. Recently, the abundant multi-omics data have provided unprecedented opportunities to discover intrinsic subtypes at an integrative level. However, it still remains a challenging task to combine these heterogeneous data and reveal the underlying clustering patterns. In this paper, we propose a novel method to identify cancer subtypes based on multi-view subspace clustering with adaptive local structure learning. Specifically, our model can effectively explore the local similarity structure from each omics data and simultaneously discover the unified consensus similarity structure among all omics data. Moreover, we design an efficient Augmented Lagrangian Multiplier algorithm to optimize the proposed framework. The experimental results on both cancer datasets and image datasets confirmed the superior performance of the proposed method over existing alternatives. In addition, we conduct a case study on melanoma and identify a set of molecular functions specific to each subtype.