Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
17 07 2020
17 07 2020
Historique:
received:
07
01
2020
accepted:
22
06
2020
entrez:
19
7
2020
pubmed:
19
7
2020
medline:
20
9
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 32681003
doi: 10.1038/s41467-020-17388-x
pii: 10.1038/s41467-020-17388-x
pmc: PMC7368050
doi:
Types de publication
Evaluation Study
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3575Subventions
Organisme : NHGRI NIH HHS
ID : R01 HG008126
Pays : United States
Organisme : NICHD NIH HHS
ID : DP2 HD091799
Pays : United States
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