Cancer subtype identification by consensus guided graph autoencoders

Cancer subtype identification aims to divide cancer patients into subgroups with distinct clinical phenotypes and facilitate the development for subgroup specific therapies. The massive amount of multi-omics datasets accumulated in the public databases have provided unprecedented opportunities to fulfill this task. As a result, great computational efforts have been made to accurately identify cancer subtypes via integrative analysis of these multi-omics datasets.

In this article, we propose a Consensus Guided Graph Autoencoder (CGGA) to effectively identify cancer subtypes. First, we learn for each omic a new feature matrix by using graph autoencoders, where both structure information and node features can be effectively incorporated during the learning process. Second, we learn a set of omic-specific similarity matrices together with a consensus matrix based on the features obtained in the first step. The learned omic-specific similarity matrices are then fed back to the graph autoencoders to guide the feature learning. By iterating the two steps above, our method obtains a final consensus similarity matrix for cancer subtyping. To comprehensively evaluate the prediction performance of our method, we compare CGGA with several approaches ranging from general-purpose multi-view clustering algorithms to multi-omics-specific integrative methods. The experimental results on both generic datasets and cancer datasets confirm the superiority of our method. Moreover, we validate the effectiveness of our method in leveraging multi-omics datasets to identify cancer subtypes. In addition, we investigate the clinical implications of the obtained clusters for glioblastoma and provide new insights into the treatment for patients with different subtypes.

The source code of our method is freely available at https://github.com/alcs417/CGGA.

Supplementary data are available at Bioinformatics online.