  Defining subtypes of complex diseases such as cancer and stratifying patient
groups with the same disease but different subtypes for targeted treatments is
important for personalized and precision medicine. Approaches that incorporate
multi-omic data are more advantageous to those using only one data type for
patient clustering and disease subtype discovery. However, it is challenging to
integrate multi-omic data as they are heterogeneous and noisy. In this paper,
we present Affinity Network Fusion (ANF) to integrate multi-omic data for
patient clustering. ANF first constructs patient affinity networks for each
omic data type, and then calculates a fused network for spectral clustering. We
applied ANF to a processed harmonized cancer dataset downloaded from GDC data
portal consisting of 2193 patients, and generated promising results on
clustering patients into correct disease types. Moreover, we developed a
semi-supervised model combining ANF and neural network for few-shot learning.
In several cases, the model can achieve greater than 90% acccuracy on test set
with training less than 1% of the data. This demonstrates the power of ANF in
learning a good representation of patients, and shows the great potential of
semi-supervised learning in cancer patient clustering.
