sigAssignmentLasso {SparseSignatures} | R Documentation

## sigAssignmentLasso

### 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)

[Package SparseSignatures version 2.0.0 Index]