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  The Distance Precision Matrix: computing networks from non-linear relationships

Ghanbari, M., Lasserre, J., & Vingron, M. (2019). The Distance Precision Matrix: computing networks from non-linear relationships. Bioinformatics, 35(6), 1009-1017. doi:10.1093/bioinformatics/bty724.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-73F0-C Version Permalink: http://hdl.handle.net/21.11116/0000-0003-73F1-B
Genre: Journal Article

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 Creators:
Ghanbari, Mahsa1, Author              
Lasserre, Julia1, Author              
Vingron, Martin2, Author              
Affiliations:
1Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              
2Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

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 Abstract: Motivation: Full-order partial correlation, a fundamental approach for network reconstruction, e.g. in the context of gene regulation, relies on the precision matrix (the inverse of the covariance matrix) as an indicator of which variables are directly associated. The precision matrix assumes Gaussian linear data and its entries are zero for pairs of variables that are independent given all other variables. However, there is still very little theory on network reconstruction under the assumption of non-linear interactions among variables. Results: We propose Distance Precision Matrix, a network reconstruction method aimed at both linear and non-linear data. Like partial distance correlation, it builds on distance covariance, a measure of possibly non-linear association, and on the idea of full-order partial correlation, which allows to discard indirect associations. We provide evidence that the Distance Precision Matrix method can successfully compute networks from linear and non-linear data, and consistently so across different datasets, even if sample size is low. The method is fast enough to compute networks on hundreds of nodes. Availability: An R package DPM is available at https://github.molgen.mpg.de/ghanbari/DPM. Supplementary information: Supplementary data are available at Bioinformatics online.

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Language(s): eng - English
 Dates: 2018-08-252019-03-15
 Publication Status: Published in print
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 Identifiers: DOI: 10.1093/bioinformatics/bty724
ISSN: 1367-4811 (Electronic)1367-4803 (Print)
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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: 9 Volume / Issue: 35 (6) Sequence Number: - Start / End Page: 1009 - 1017 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991