BASCULE: Bayesian inference and clustering of mutational signatures leveraging biological priors

In the preprint available at https://doi.org/10.1101/2024.09.16.613266 we present BASCULE, a new method to perform Bayesian signatures deconvolution and to cluster patients from the inferred exposures. The method can deconvolve any kind of mutational signature types (SBS, DBS, ID, etc.) including as input a reference catalogue of known signatures (i.e., COSMIC), and cluster the samples joinltly from the exposures of all signature types. BASCULE is available as an R package (https://github.com/caravagnalab/bascule.git). Here we release the data and code to reproduce the analysis on synthetic and real datasets presented in the preprint, in the "synthetic_data_validation.zip" and "real_data_validation.zip", respectively.