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RoDiCE: Robust differential protein co-expression analysis for cancer complexome

View ORCID ProfileYusuke Matsui, Yuichi Abe, Kohei Uno, Satoru Miyano
doi: https://doi.org/10.1101/2020.12.22.423973
Yusuke Matsui
1Biomedical and Health Informatics Unit, Department of Integrated Health Science, Nagoya University Graduate School of Medicine
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  • For correspondence: matsui@met.nagoya-u.ac.jp
Yuichi Abe
2Division of Molecular Diagnostics, Aichi Cancer Center Research Institute
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Kohei Uno
1Biomedical and Health Informatics Unit, Department of Integrated Health Science, Nagoya University Graduate School of Medicine
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Satoru Miyano
3Department of Integrated Data Science, M&D Data Science Center, Tokyo Medical and Dental University
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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.

Footnotes

  • https://github.com/ymatts/RoDiCE

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 23, 2020.
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RoDiCE: Robust differential protein co-expression analysis for cancer complexome
Yusuke Matsui, Yuichi Abe, Kohei Uno, Satoru Miyano
bioRxiv 2020.12.22.423973; doi: https://doi.org/10.1101/2020.12.22.423973
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RoDiCE: Robust differential protein co-expression analysis for cancer complexome
Yusuke Matsui, Yuichi Abe, Kohei Uno, Satoru Miyano
bioRxiv 2020.12.22.423973; doi: https://doi.org/10.1101/2020.12.22.423973

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