Perform the discovery of K somatic mutational signatures given a set of observed counts x.
nmfLasso(
x,
K,
beta = NULL,
background_signature = NULL,
normalize_counts = TRUE,
nmf_runs = 10,
lambda_rate_alpha = 0.05,
lambda_rate_beta = 0.05,
iterations = 30,
max_iterations_lasso = 10000,
seed = NULL,
verbose = TRUE
)count matrix for a set of n patients and 96 trinucleotides.
numeric value (minimum 2) indicating the number of signatures to be discovered.
starting beta for the estimation. If it is NULL, starting beta is estimated by NMF.
background signature to be used. If not provided, a warning is thrown and an initial value for it is estimated by NMF. If beta is not NULL, this parameter is ignored.
if true, the input count matrix x is normalize such that the patients have the same number of mutation.
number of iteration (minimum 1) of NMF to be performed for a robust estimation of starting beta. If beta is not NULL, this parameter is ignored.
value of LASSO to be used for alpha between 0 and 1. This value should be greater than 0. 1 is the value of LASSO that would shrink all the exposure values to 0 within one step. The higher lambda_rate_alpha is, the sparser are the resulting exposure values, but too large values may result in a reduced fit of the observed counts.
value of LASSO to be used for beta between 0 and 1. This value should be greater than 0. 1 is the value of LASSO that would shrink all the signatures to 0 within one step. The higher lambda_rate_beta is, the sparser are the resulting signatures, but too large values may result in a reduced fit of the observed counts.
Number of iterations to be performed. Each iteration corresponds to a first step where beta is fitted and a second step where alpha is fitted.
Number of maximum iterations to be performed during the sparsification via Lasso.
Seed for reproducibility.
boolean; Shall I print all messages?
A list with the discovered signatures. It includes 6 elements: alpha: matrix of the discovered exposure values beta: matrix of the discovered signatures starting_alpha: initial alpha on which the method has been applied starting_beta: initial beta on which the method has been applied loglik_progression: log-likelihood values during the iterations. This values should be increasing, if not the selected value of lambda is too high best_loglik: log-likelihood of the best signatures configuration
data(patients)
data(starting_betas_example)
beta = starting_betas_example[["5_signatures","Value"]]
res = nmfLasso(x=patients[1:100,],
K=5,
beta=beta,
lambda_rate_alpha=0.05,
lambda_rate_beta=0.05,
iterations=5,
seed=12345)
#> Performing the discovery of the signatures by NMF with Lasso...
#> Performing a total of 5 iterations...
#> Progress 20%...
#> Progress 40%...
#> Progress 60%...
#> Progress 80%...
#> Progress 100%...