Description Usage Arguments Value Examples
View source: R/discovery_prediction.R
Mutational signatures and exposures will be discovered using
methods such as Latent Dirichlet Allocation (lda) or Non-Negative
Matrix Factorization (nmf). These algorithms will deconvolute a matrix of
counts for mutation types in each sample to two matrices: 1) a "signature"
matrix containing the probability of each mutation type in each sample and
2) an "exposure" matrix containing the estimated counts for each signature
in each sample. Before mutational discovery can be performed,
variants from samples first need to be stored in a
musica
object using the create_musica function
and mutation count tables need to be created using functions such as
build_standard_table.
1 2 3 4 5 6 7 8 9 | discover_signatures(
musica,
table_name,
num_signatures,
method = "lda",
seed = 1,
nstart = 10,
par_cores = FALSE
)
|
musica |
A |
table_name |
Name of the table to use for signature discovery. Needs to be the same name supplied to the table building functions such as build_standard_table. |
num_signatures |
Number of signatures to discover. |
method |
Method to use for mutational signature discovery. One of
|
seed |
Seed to be used for the random number generators in the
signature discovery algorithms. Default |
nstart |
Number of independent random starts used in the mutational
signature algorithms. Default |
par_cores |
Number of parallel cores to use. Only used if
|
Returns a A musica_result
object containing
signatures and exposures.
1 2 3 4 5 | data(musica)
g <- select_genome("19")
build_standard_table(musica, g, "SBS96", overwrite = TRUE)
discover_signatures(musica = musica, table_name = "SBS96",
num_signatures = 3, method = "lda", seed = 12345, nstart = 1)
|
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