fit_to_signatures {MutationalPatterns} | R Documentation

## Find optimal nonnegative linear combination of mutation signatures to reconstruct the mutation matrix.

### Description

Find the linear combination of mutation signatures that most closely reconstructs the mutation matrix by solving the nonnegative least-squares constraints problem.

### Usage

    fit_to_signatures(mut_matrix, signatures)

### Arguments

`mut_matrix` | 96 mutation count matrix (dimensions: 96 mutations X n samples)
`signatures` | Signature matrix (dimensions: 96 mutations X n signatures)

### Value

Named list with signature contributions and reconstructed mutation matrix

### See Also

`mut_matrix`

### Examples

    ## See the 'mut_matrix()' example for how we obtained the mutation matrix:
    mut_mat <- readRDS(system.file("states/mut_mat_data.rds",
                        package="MutationalPatterns"))

    ## You can download the signatures from the COSMIC website:
    # http://cancer.sanger.ac.uk/cancergenome/assets/signatures_probabilities.txt

    ## We copied the file into our package for your convenience.
    filename <- system.file("extdata/signatures_probabilities.txt",
                            package="MutationalPatterns")
    cancer_signatures <- read.table(filename, sep = "\t", header = TRUE)

    ## Match the order to MutationalPatterns standard of mutation matrix
    order = match(row.names(mut_mat), cancer_signatures$Somatic.Mutation.Type)
    ## Reorder cancer signatures dataframe
    cancer_signatures = cancer_signatures[order,]
    ## Use trinucletiode changes names as row.names
    ## row.names(cancer_signatures) = cancer_signatures$Somatic.Mutation.Type
    ## Keep only 96 contributions of the signatures in matrix
    cancer_signatures = as.matrix(cancer_signatures[,4:33])
    ## Rename signatures to number only
    colnames(cancer_signatures) = as.character(1:30)

    ## Perform the fitting
    fit_res <- fit_to_signatures(mut_mat, cancer_signatures)

[Package MutationalPatterns version 2.0.0 Index]