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- W4284887012 abstract "Somatic mutations in cancer can be viewed as a mixture distribution of several mutational signatures, which can be inferred using non-negative matrix factorization (NMF). Mutational signatures have previously been parametrized using either simple mono-nucleotide interaction models or general tri-nucleotide interaction models. We describe a flexible and novel framework for identifying biologically plausible parametrizations of mutational signatures, and in particular for estimating di-nucleotide interaction models. The estimation procedure is based on the expectation--maximization (EM) algorithm and regression in the log-linear quasi--Poisson model. We show that di-nucleotide interaction signatures are statistically stable and sufficiently complex to fit the mutational patterns. Di-nucleotide interaction signatures often strike the right balance between appropriately fitting the data and avoiding over-fitting. They provide a better fit to data and are biologically more plausible than mono-nucleotide interaction signatures, and the parametrization is more stable than the parameter-rich tri-nucleotide interaction signatures. We illustrate our framework on three data sets of somatic mutation counts from cancer patients." @default.
- W4284887012 created "2022-07-09" @default.
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- W4284887012 date "2022-07-06" @default.
- W4284887012 modified "2023-09-29" @default.
- W4284887012 title "A flexible model-based framework for robust estimation of mutational signatures" @default.
- W4284887012 doi "https://doi.org/10.48550/arxiv.2207.02677" @default.
- W4284887012 hasPublicationYear "2022" @default.
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