Perform a robust estimation of the starting betas for the nmfLasso method

startingBetaEstimation(
  x,
  K = 3:10,
  background_signature = NULL,
  normalize_counts = TRUE,
  nmf_runs = 10,
  seed = NULL,
  verbose = TRUE
)

Arguments

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.

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.

seed

Seed for reproducibility.

verbose

boolean; Shall I print all messages?

Value

A list containing the starting beta values for each configuration of K.

Examples

data(background)
data(patients)
res = startingBetaEstimation(x=patients[1:100,],
     K=3:5,
     background_signature=background,
     nmf_runs=1,
     seed=12345)
#> Performing a robust estimation of the starting betas for the nmfLasso method... 
#> Computing the initial values of beta by standard NMF... 
#> Progress 33.333%... 
#> Computing the initial values of beta by standard NMF... 
#> Progress 66.667%... 
#> Computing the initial values of beta by standard NMF... 
#> Progress 100%...