signaturesAssignment

Perform the assignment of K somatic mutational signatures provided as input to samples given a set of observed counts x. This function can be used to estimate different types of mutational signatures such as: SBS (single base substitutions) and MNV (multi-nucleotide variant) (see Degasperi, Andrea, et al. 'Substitution mutational signatures in whole-genome–sequenced cancers in the UK population.' Science 376.6591 (2022): abl9283), CX (chromosomal instability) (see Drews, Ruben M., et al. 'A pan-cancer compendium of chromosomal instability.' Nature 606.7916 (2022): 976-983) and CN (copy number) signatures (see Steele, Christopher D., et al. 'Signatures of copy number alterations in human cancer.' Nature 606.7916 (2022): 984-991).

signaturesAssignment(x, beta)

Arguments

x

Counts matrix for a set of n patients and m categories. These can be, e.g., SBS, MNV, CN or CN counts; in the case of SBS it would be an n patients x 96 trinucleotides matrix.

beta

Matrix of the discovered signatures to be used for the assignment.

Value

A list with the discovered signatures. It includes 3 elements: alpha: matrix of the discovered exposure values. beta: matrix of the discovered signatures. unexplained_mutations: number of unexplained mutations per sample.

Examples

data(background)
data(patients)
set.seed(12345)
beta <- signaturesDecomposition(x = patients[seq_len(3),seq_len(2)],
                                K = 3,
                                background_signature = background[seq_len(2)],
                                nmf_runs = 2,
                                num_processes = 1)
#> Performing signatures discovery and rank estimation...
#> Performing inference for K=3...
#> Performing NMF run 1 out of 2...
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Performing NMF run 2 out of 2...
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
#> Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold
set.seed(12345)
res <- signaturesAssignment(x = patients[seq_len(3),seq_len(2)], beta = beta$beta[[1]])