We outline the features of the R package SparseSignatures and its application to determine the signatures contributing to mutation profiles of tumor samples. We describe installation details and illustrate a step-by-step approach to (1) prepare the data for signature analysis, (2) determine the optimal parameters, and (3) employ them to determine the signatures and related exposure levels in the point mutation dataset. For complete details on the use and execution of this protocol, please refer to Lal et al. (2021).
Mella, L., Lal, A., Angaroni, F., Maspero, D., Piazza, R., Sidow, A., et al. (2022). SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples. STAR PROTOCOLS, 3(3) [10.1016/j.xpro.2022.101513].
SparseSignatures: An R package using LASSO-regularized non-negative matrix factorization to identify mutational signatures from human tumor samples
Angaroni F.;Maspero D.;Piazza R.;Antoniotti M.
;Graudenzi A.
;Ramazzotti D.
2022
Abstract
We outline the features of the R package SparseSignatures and its application to determine the signatures contributing to mutation profiles of tumor samples. We describe installation details and illustrate a step-by-step approach to (1) prepare the data for signature analysis, (2) determine the optimal parameters, and (3) employ them to determine the signatures and related exposure levels in the point mutation dataset. For complete details on the use and execution of this protocol, please refer to Lal et al. (2021).
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