fit_to_signatures {MutationalPatterns} | R Documentation |
Find the linear combination of mutation signatures that most closely reconstructs the mutation matrix by solving the nonnegative least-squares constraints problem.
fit_to_signatures(mut_matrix, signatures)
mut_matrix |
96 mutation count matrix (dimensions: 96 mutations X n samples) |
signatures |
Signature matrix (dimensions: 96 mutations X n signatures) |
Named list with signature contributions and reconstructed mutation matrix
## 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)