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Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood

Author

Listed:
  • Shantao Li

    (Yale University
    Yale University)

  • Forrest W. Crawford

    (Yale School of Public Health
    Yale University
    Yale School of Management
    Yale University)

  • Mark B. Gerstein

    (Yale University
    Yale University
    Yale University
    Yale University)

Abstract

Multiple mutational processes drive carcinogenesis, leaving characteristic signatures in tumor genomes. Determining the active signatures from a full repertoire of potential ones helps elucidate mechanisms of cancer development. This involves optimally decomposing the counts of cancer mutations, tabulated according to their trinucleotide context, into a linear combination of known signatures. Here, we develop sigLASSO (a software tool at github.com/gersteinlab/siglasso ) to carry out this optimization efficiently. sigLASSO has four key aspects: (1) It jointly optimizes the likelihood of sampling and signature fitting, by explicitly factoring multinomial sampling into the objective function. This is particularly important when mutation counts are low and sampling variance is high (e.g., in exome sequencing). (2) sigLASSO uses L1 regularization to parsimoniously assign signatures, leading to sparse and interpretable solutions. (3) It fine-tunes model complexity, informed by data scale and biological priors. (4) Consequently, sigLASSO can assess model uncertainty and abstain from making assignments in low-confidence contexts.

Suggested Citation

  • Shantao Li & Forrest W. Crawford & Mark B. Gerstein, 2020. "Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17388-x
    DOI: 10.1038/s41467-020-17388-x
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    Cited by:

    1. Matúš Medo & Charlotte K. Y. Ng & Michaela Medová, 2024. "A comprehensive comparison of tools for fitting mutational signatures," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    2. Qi Zhao & Feng Wang & Yan-Xing Chen & Shifu Chen & Yi-Chen Yao & Zhao-Lei Zeng & Teng-Jia Jiang & Ying-Nan Wang & Chen-Yi Wu & Ying Jing & You-Sheng Huang & Jing Zhang & Zi-Xian Wang & Ming-Ming He & , 2022. "Comprehensive profiling of 1015 patients’ exomes reveals genomic-clinical associations in colorectal cancer," Nature Communications, Nature, vol. 13(1), pages 1-17, December.

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