Cancer Subtype Identification through Integrating Inter and Intra
  Dataset Relationships in Multi-Omics Data
Mark Peelen Leila Bagheriye Johan Kwisthout
The integration of multi-omics data has emerged as a promising approach for
gaining comprehensive insights into complex diseases such as cancer. This paper
proposes a novel approach to identify cancer subtypes through the integration
of multi-omics data for clustering. The proposed method, named LIDAF utilises
affinity matrices based on linear relationships between and within different
omics datasets (Linear Inter and Intra Dataset Affinity Fusion (LIDAF)).
Canonical Correlation Analysis is in this paper employed to create distance
matrices based on Euclidean distances between canonical variates. The distance
matrices are converted to affinity matrices and those are fused in a three-step
process. The proposed LIDAF addresses the limitations of the existing method
resulting in improvement of clustering performance as measured by the Adjusted
Rand Index and the Normalized Mutual Information score. Moreover, our proposed
LIDAF approach demonstrates a notable enhancement in 50% of the log10 rank
p-values obtained from Cox survival analysis, surpassing the performance of the
best reported method, highlighting its potential of identifying distinct cancer
subtypes.