  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.
