Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization.

ABSTRACT:

Background

The spectrum of mutations in a collection of cancer genomes can be described by a mixture of a few mutational signatures. The mutational signatures can be found using non-negative matrix factorization (NMF). To extract the mutational signatures we have to assume a distribution for the observed mutational counts and a number of mutational signatures. In most applications, the mutational counts are assumed to be Poisson distributed, and the rank is chosen by comparing the fit of several models with the same underlying distribution and different values for the rank using classical model selection procedures. However, the counts are often overdispersed, and thus the Negative Binomial distribution is more appropriate.

Results

We propose a Negative Binomial NMF with a p

SUBMITTER: Pelizzola M 

PROVIDER: S-EPMC10165836 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature