Integrate Multi-omic Data Using Affinity Network Fusion (ANF) for Cancer Patient Clustering

TL;DR

This paper introduces Affinity Network Fusion (ANF), an improved method over SNF, for integrating multi-omic data to effectively cluster cancer patients and potentially discover new subtypes.

Contribution

ANF enhances similarity network fusion by providing a more effective way to integrate heterogeneous omic data for cancer patient clustering.

Findings

ANF successfully clusters patients into correct disease types.

Feature selection improves clustering performance.

Affinity matrices reveal potential new cancer subtypes.

Abstract

Clustering cancer patients into subgroups and identifying cancer subtypes is an important task in cancer genomics. Clustering based on comprehensive multi-omic molecular profiling can often achieve better results than those using a single data type, since each omic data type (representing one view of patients) may contain complementary information. However, it is challenging to integrate heterogeneous omic data types directly. Based on one popular method -- Similarity Network Fusion (SNF), we presented Affinity Network Fusion (ANF) in this paper, an "upgrade" of SNF with several advantages. Similar to SNF, ANF treats each omic data type as one view of patients and learns a fused affinity (transition) matrix for clustering. We applied ANF to a carefully processed harmonized cancer dataset downloaded from GDC data portals consisting of 2193 patients, and generated promising results on clustering patients into correct disease types. Our experimental results also demonstrated the power of feature selection and transformation combined with using ANF in patient clustering. Moreover, eigengap analysis suggests that the learned affinity matrices of four cancer types using our proposed framework may have successfully captured patient group structure and can be used for discovering unknown cancer subtypes.