View source: R/extract_signatures.R
extract_signatures | R Documentation |
Decomposes trinucleotide count matrix into signatures and contribution of those signatures to the spectra of the samples/vcf files.
extract_signatures( mut_matrix, rank, nrun = 200, nmf_type = c("regular", "variational_bayes"), single_core = FALSE, fudge = NULL, seed = 123456 )
mut_matrix |
96 mutation count matrix |
rank |
Number of signatures to extract |
nrun |
Number of iterations, default = 200. A lower number will be faster, but result in less accurate results. |
nmf_type |
Type of NMF to be used. Possible values: * 'regular' * 'variational_bayes' The 'regular' method comes from the NMF package. The 'variational_bayes' method comes from the ccfindR package. This method uses bayesian inference, which makes it easier to determine the mathematically optimal number of signatures. |
single_core |
Boolean. If TRUE, it forces the NMF algorithm to use only a single core. This can sometimes prevent issues. Doesn't apply to variational-bayes NMF |
fudge |
Small positive number that is used for the variational_bayes NMF. Setting this to a small value like 0.0001 can prevent errors from occurring, when extracting many signatures at once. In general, we recommend extracting less signatures when errors occur, but this parameter can be used when that is not an option. Default = NULL. |
seed |
Random seed used for the regular NMF, default = 123456 |
Named list of mutation matrix, signatures and signature contribution
mut_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" )) ## This function is computationally intensive. # nmf_res <- extract_signatures(mut_mat, rank = 2) ## It's also possible to use a variational Bayes method. ## It requires the ccfindR package to work. # nmf_res <- extract_signatures(mut_mat, rank = 2, nmf_type = "variational_bayes")
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