sigAssignmentCV: sigAssignmentCV
In SparseSignatures: SparseSignatures

Description Usage Arguments Value Examples

Perform the assessment of different sigAssignmentLasso solutions by cross validation for a given set of somatic mutational signatures and observations x. The estimation can slow down because of memory usage and intensive computations, when a big number of cross validation repetitions is asked and when the grid search is performed for a lot of configurations. In this case, an advice may be to split the computation into multiple smaller sets.

sigAssignmentCV(
  x,
  beta,
  normalize_counts = TRUE,
  lambda_values_alpha = c(0, 0.01, 0.05, 0.1),
  cross_validation_entries = 0.01,
  cross_validation_iterations = 5,
  cross_validation_repetitions = 50,
  max_iterations_lasso = 10000,
  num_processes = Inf,
  seed = NULL,
  verbose = TRUE,
  log_file = ""
)

`x`	count matrix for a set of n patients and 96 trinucleotides.
`beta`	beta to be fixed during the estimation of alpha.
`normalize_counts`	if true, the input count matrix x is normalize such that the patients have the same number of mutation.
`lambda_values_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 signatures to 0 within one step. The higher lambda_rate_alpha is, the sparser are the resulting exposures, but too large values may result in a reduced fit of the observed counts.
`cross_validation_entries`	Percentage of cells in the count matrix to be replaced by 0s during cross validation.
`cross_validation_iterations`	For each configuration, the first time the signatures are discovered form a matrix with a percentage of values replaced by 0s. This may result in poor fit/results. Then, we perform predictions of these entries and replace them with such predicted values. This parameter is the number of restarts to be performed to improve this estimate and obtain more stable solutions.
`cross_validation_repetitions`	Number of time cross-validation should be repeated. Higher values result in better estimate, but are computationally more expensive.
`max_iterations_lasso`	Number of maximum iterations to be performed during the sparsification via Lasso.
`num_processes`	Number of processes to be used during parallel execution. To execute in single process mode, this parameter needs to be set to either NA or NULL.
`seed`	Seed for reproducibility.
`verbose`	boolean; Shall I print all messages?
`log_file`	log file where to print outputs when using parallel. If parallel execution is disabled, this parameter is ignored.

A list of 2 elements: grid_search_mse and and grid_search_loglik. Here, grid_search_mse reports the mean squared error for each configuration of performed cross validation; grid_search_loglik reports for each configuration the number of times the algorithm converged.

data(patients)
data(starting_betas_example)
beta = starting_betas_example[["5_signatures","Value"]]
res = sigAssignmentCV(x=patients[1:100,],
     beta=beta,
     lambda_values_alpha=c(0.00),
     cross_validation_repetitions=2,
     num_processes=NA,
     seed=12345)