nmfLasso: nmfLasso
In SparseSignatures: SparseSignatures

Description Usage Arguments Value Examples

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
)

`x`	count matrix for a set of n patients and 96 trinucleotides.
`K`	numeric value (minimum 2) indicating the number of signatures to be discovered.
`beta`	starting beta for the estimation. If it is NULL, starting beta is estimated by NMF.
`background_signature`	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.
`normalize_counts`	if true, the input count matrix x is normalize such that the patients have the same number of mutation.
`nmf_runs`	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.
`lambda_rate_alpha`	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.
`lambda_rate_beta`	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.
`iterations`	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.
`max_iterations_lasso`	Number of maximum iterations to be performed during the sparsification via Lasso.
`seed`	Seed for reproducibility.
`verbose`	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)