MOTIVATION: Mutational signatures can be used to understand cancer origins and 
provide a unique opportunity to group tumor types that share the same origins 
and result from similar processes. These signatures have been identified from 
high throughput sequencing data generated from cancer genomes by using 
non-negative matrix factorisation (NMF) techniques. Current methods based on 
optimization techniques are strongly sensitive to initial conditions due to high 
dimensionality and nonconvexity of the NMF paradigm. In this context, an 
important question consists in the determination of the actual number of 
signatures that best represent the data. The extraction of mutational signatures 
from high-throughput data still remains a daunting task.
RESULTS: Here we present a new method for the statistical estimation of 
mutational signatures based on an empirical Bayesian treatment of the NMF model. 
While requiring minimal intervention from the user, our method addresses the 
determination of the number of signatures directly as a model selection problem. 
In addition, we introduce two new concepts of significant clinical relevance for 
evaluating the mutational profile. The advantages brought by our approach are 
shown by the analysis of real and synthetic data. The later is used to compare 
our approach against two alternative methods mostly used in the literature and 
with the same NMF parametrization as the one considered here. Our approach is 
robust to initial conditions and more accurate than competing alternatives. It 
also estimates the correct number of signatures even when other methods fail. 
Results on real data agree well with current knowledge.
AVAILABILITY AND IMPLEMENTATION: signeR is implemented in R and C ++, and is 
available as a R package at http://bioconductor.org/packages/signeR CONTACT: 
itojal@cipe.accamargo.org.brSupplementary information: Supplementary data are 
available at Bioinformatics online.