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sigLASSO: optimizing cancer mutation signatures jointly with sampling likelihood

View ORCID ProfileShantao Li, View ORCID ProfileForrest W. Crawford, View ORCID ProfileMark B. Gerstein
doi: https://doi.org/10.1101/366740
Shantao Li
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
2Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
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Forrest W. Crawford
3Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
4Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
5Yale School of Management, New Haven, CT, USA
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Mark B. Gerstein
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
2Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
6Department of Computer Science, Yale University, New Haven, CT, USA
7Department of Statistics and Data Science, Yale University, New Haven, CT, USA
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  • For correspondence: pi@gersteinlab.org
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Abstract

Multiple mutational processes drive carcinogenesis, leaving characteristic signatures on tumor genomes. Determining the active signatures from the full repertoire of potential ones can help elucidate mechanisms underlying cancer initiation and development. This task in-volves decomposing the counts of cancer mutations, tabulated according to their trinucleotide context, into a linear combination of known mutational signatures. We formulate it as an optimization problem and develop sigLASSO, a software tool, to carry it out efficiently. (An R package implementation is available at github.com/gersteinlab/siglasso). sigLASSO features four key aspects: (1) It jointly optimizes the likelihood of sampling and signature fitting, by explicitly adding multinomial sampling into the overall objective function. This is particularly important when mutation counts are low and sampling variance is high, such as in exome sequencing. (2) sigLASSO uses L1 regularization to parsimoniously assign signatures to mutation profiles, leading to sparse and more biologically interpretable solutions resembling previously well-characterized results. (3) sigLASSO fine-tunes model complexity, informed by the scale of the data and biological-knowledge based priors. In particular, instead of hard thresholding and choosing a priori a discrete subset of active signatures, sigLASSO allows continuous priors, which can be effectively learned from auxiliary information. (4) Because of this, sigLASSO can assess model uncertainty and abstain from making certain assignments in low-confidence contexts. Finally, to evaluate sigLASSO signature assignments in comparison to other approaches, we develop a set of reasonable expectations (e.g. sparsity, the ability to abstain, and robustness to noise) that we apply consistently in a variety of contexts.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/gersteinlab/sigLASSO

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted May 01, 2020.
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sigLASSO: optimizing cancer mutation signatures jointly with sampling likelihood
Shantao Li, Forrest W. Crawford, Mark B. Gerstein
bioRxiv 366740; doi: https://doi.org/10.1101/366740
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sigLASSO: optimizing cancer mutation signatures jointly with sampling likelihood
Shantao Li, Forrest W. Crawford, Mark B. Gerstein
bioRxiv 366740; doi: https://doi.org/10.1101/366740

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