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.
As it is challenging to define a proper prior distribution and learn a good posterior distribution with limited data, many other approaches do not use probabilistic modeling. For example, Similarity Network Fusion (SNF) @cite_8 constructs patient similarity networks using different types of omic features, and fuses multiple patient similarity networks to achieve a consensus'' network that is then used for clustering patients into disease subtypes.
@cite_8
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Similarity network fusion (SNF) is an approach to integrate multiple data types on the basis of similarity between biological samples rather than individual measurements. The authors demonstrate SNF by constructing patient networks to identify disease subtypes with differential survival profiles.