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

TL;DR

This paper introduces a Negative Binomial non-negative matrix factorization method with a novel model selection procedure for more accurately identifying mutational signatures in cancer genomes, especially under overdispersion.

Contribution

It proposes a Negative Binomial NMF with patient-specific dispersion parameters and a new cross-validation inspired model selection method for robust mutational signature extraction.

Findings

The method outperforms classical approaches in simulations under model misspecification.

It more accurately determines the true number of signatures in overdispersed data.

Applied to real cancer datasets, it reveals meaningful mutational signatures.

Abstract

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. We propose a Negative Binomial NMF with a patient specific dispersion parameter to capture the variation across patients. We also introduce a novel model selection procedure inspired by cross-validation to determine the number of signatures. Using simulations, we study the influence of the distributional assumption on our method together with other classical model selection procedures and we show that our model selection procedure is more robust at determining the correct number of signatures under model misspecification. We also show that our model selection procedure is more accurate than state-of-the-art methods for finding the true number of signatures. Other methods are highly overestimating the number of signatures when overdispersion is present. We apply our proposed analysis on a wide range of simulated data and on two real data sets from breast and prostate cancer patients. The code for our model selection procedure and negative binomial NMF is available in the R package SigMoS and can be found at https://github.com/MartaPelizzola/SigMoS.