sigAssignmentLasso {SparseSignatures} | R Documentation |
Perform the assignment of somatic mutational signatures to patients given a set of observed counts x and signatures beta.
sigAssignmentLasso( x, beta, normalize_counts = TRUE, lambda_rate_alpha = 0.05, max_iterations_lasso = 10000, seed = NULL, verbose = TRUE )
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? |
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
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)