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BiCoN: Network-constrained biclustering of patients and omics data

View ORCID ProfileOlga Lazareva, View ORCID ProfileHoan Van Do, View ORCID ProfileStefan Canzar, View ORCID ProfileKevin Yuan, View ORCID ProfileJan Baumbach, View ORCID ProfilePaolo Tieri, View ORCID ProfileTim Kacprowski, View ORCID ProfileMarkus List
doi: https://doi.org/10.1101/2020.01.31.926345
Olga Lazareva
1Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich 80333, Germany
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  • For correspondence: olga.lazareva@tum.de
Hoan Van Do
2Gene Center, Ludwig-Maximilians-University of Munich, Munich 81377, Germany
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Stefan Canzar
2Gene Center, Ludwig-Maximilians-University of Munich, Munich 81377, Germany
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Kevin Yuan
1Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich 80333, Germany
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Jan Baumbach
1Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich 80333, Germany
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Paolo Tieri
3CNR National Research Council, IAC Institute for Applied Computing, Via dei Taurini 19, Rome, Italy
4Data Science Program, La Sapienza University of Rome, Rome, Italy
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Tim Kacprowski
1Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich 80333, Germany
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Markus List
1Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich 80333, Germany
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Abstract

Motivation Unsupervised learning approaches are frequently employed to identify patient subgroups and biomarkers such as disease-associated genes. Thus, clustering and biclustering are powerful techniques often used with expression data, but are usually not suitable to unravel molecular mechanisms along with patient subgroups. To alleviate this, we developed the network-constrained biclustering approach BiCoN (Biclustering Constrained by Networks) which (i) restricts biclusters to functionally related genes connected in molecular interaction networks and (ii) maximizes the difference in gene expression between two subgroups of patients.

Results Our analyses of non-small cell lung and breast cancer gene expression data demonstrate that BiCoN clusters patients in agreement with known cancer subtypes while discovering gene subnetworks pointing to functional differences between these subtypes. Furthermore, we show that BiCoN is robust to noise and batch effects and can distinguish between high and low load of tumor-infiltrating leukocytes while identifying subnetworks related to immune cell function. In summary, BiCoN is a powerful new systems medicine tool to stratify patients while elucidating the responsible disease mechanism.

Availability PyPI package: https://pypi.org/project/bicon

Web interface: https://exbio.wzw.tum.de/bicon

Contact olga.lazareva{at}tum.de

Supplementary information Supplementary data are available at Bioinformatics online.

Footnotes

  • ↵† Joint last authorship

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 March 25, 2020.
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BiCoN: Network-constrained biclustering of patients and omics data
Olga Lazareva, Hoan Van Do, Stefan Canzar, Kevin Yuan, Jan Baumbach, Paolo Tieri, Tim Kacprowski, Markus List
bioRxiv 2020.01.31.926345; doi: https://doi.org/10.1101/2020.01.31.926345
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BiCoN: Network-constrained biclustering of patients and omics data
Olga Lazareva, Hoan Van Do, Stefan Canzar, Kevin Yuan, Jan Baumbach, Paolo Tieri, Tim Kacprowski, Markus List
bioRxiv 2020.01.31.926345; doi: https://doi.org/10.1101/2020.01.31.926345

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