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Clustering cancer patients into subgroups and identifying cancer subtypes isan important task in cancer genomics. Clustering based on comprehensivemulti-omic molecular profiling can often achieve better results than thoseusing a single data type, since each omic data type (representing one view ofpatients) may contain complementary information. However, it is challenging tointegrate 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, ANFtreats each omic data type as one view of patients and learns a fused affinity(transition) matrix for clustering. We applied ANF to a carefully processedharmonized cancer dataset downloaded from GDC data portals consisting of 2193patients, and generated promising results on clustering patients into correctdisease types. Our experimental results also demonstrated the power of featureselection and transformation combined with using ANF in patient clustering.Moreover, eigengap analysis suggests that the learned affinity matrices of fourcancer types using our proposed framework may have successfully capturedpatient group structure and can be used for discovering unknown cancersubtypes.

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