Abstract
Motivation The full picture of abnormalities in protein complexes in cancer remains largely unknown. Comparing the co-expression structure of each protein complex between tumor and normal groups could help us understand the cancer-specific dysfunction of proteins. However, the technical limitations of mass spectrometry-based proteomics and biological variations contaminating the protein expression with noise lead to non-negligible over- (or under-) estimating co-expression.
Results We propose a robust algorithm for identifying protein complex aberrations in cancer based on differential protein co-expression testing. Our method based on a copula is sufficient for improving the identification accuracy with noisy data over a conventional linear correlation-based approach. As an application, we show that important protein complexes can be identified along with regulatory signaling pathways, and even drug targets can be identified using large-scale proteomics data from renal cancer. The proposed approach goes beyond traditional linear correlations to provide insights into higher order differential co-expression structures.
Availability and Implementation https://github.com/ymatts/RoDiCE.
Contact matsui{at}met.ngaoya-u.ac.jp
Competing Interest Statement
The authors have declared no competing interest.
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