sigAssignmentEvaluation: sigAssignmentEvaluation

In danro9685/SparseSignatures: SparseSignatures

sigAssignmentEvaluation

Description

Estimate the range of lambda values for alpha to be considered in the signature assignment. Note that too small values of lambda result in dense exposures, but too large values lead to bad fit of the counts.

Usage

sigAssignmentEvaluation(
  x,
  beta,
  normalize_counts = TRUE,
  lambda_values = c(0.01, 0.05, 0.1, 0.2),
  max_iterations_lasso = 10000,
  num_processes = Inf,
  seed = NULL,
  verbose = TRUE,
  log_file = ""
)

Arguments

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	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_values is, the sparser are the resulting exposures, but too large values may result in a reduced fit of the observed counts.
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.

Value

A list corresponding to results of the function sigAssignmentLasso for each value of lambda to be tested. This function allows to test a good range of lambda values for alpha to be considered. One should keep in mind that too small values generate dense solution, while too high ones leads to poor fit. This behavior is resampled in the values of loglik_progression, which should be increasing: too small values of lambda results in unstable log-likelihood through the iterations, while too large values make log-likelihood drop.

Examples

data(patients)
data(starting_betas_example)
beta = starting_betas_example[["5_signatures","Value"]]
res = sigAssignmentEvaluation(x=patients[1:100,],
     beta=beta,
     lambda_values=c(0.01,0.05),
     num_processes=NA,
     seed=12345)

danro9685/SparseSignatures documentation built on Nov. 1, 2024, 8:54 p.m.