sigAssignmentLasso: sigAssignmentLasso

In SparseSignatures: SparseSignatures

Description

Perform the assignment of somatic mutational signatures to patients given a set of observed counts x and signatures beta.

Usage

sigAssignmentLasso(
  x,
  beta,
  normalize_counts = TRUE,
  lambda_rate_alpha = 0.05,
  max_iterations_lasso = 10000,
  seed = NULL,
  verbose = TRUE
)

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_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.
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?

Value

A list with the discovered signatures and their assignment to patients. It includes 2 elements: alpha: matrix of the assigned exposure values beta: matrix of the discovered signatures

Examples

data(patients)
data(starting_betas_example)
beta = starting_betas_example[["5_signatures","Value"]]
res = sigAssignmentLasso(x=patients[1:100,],beta=beta,lambda_rate_alpha=0.05,seed=12345)

SparseSignatures documentation built on Nov. 8, 2020, 4:55 p.m.