PT  - JOURNAL ARTICLE
AU  - Matsui, Yusuke
AU  - Abe, Yuichi
AU  - Uno, Kohei
AU  - Miyano, Satoru
TI  - RoDiCE: Robust differential protein co-expression analysis for cancer complexome
AID  - 10.1101/2020.12.22.423973
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.12.22.423973
4099  - http://biorxiv.org/content/early/2020/12/23/2020.12.22.423973.short
4100  - http://biorxiv.org/content/early/2020/12/23/2020.12.22.423973.full
AB  - 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.jpCompeting Interest StatementThe authors have declared no competing interest.