sig_extract {sigminer} | R Documentation |
Extract Signatures through NMF
Description
Do NMF de-composition and then extract signatures.
Usage
sig_extract(
nmf_matrix,
n_sig,
nrun = 10,
cores = 1,
method = "brunet",
optimize = FALSE,
pynmf = FALSE,
use_conda = TRUE,
py_path = "/Users/wsx/anaconda3/bin/python",
seed = 123456,
...
)
Arguments
nmf_matrix |
a |
n_sig |
number of signature. Please run sig_estimate to select a suitable value. |
nrun |
a |
cores |
number of cpu cores to run NMF. |
method |
specification of the NMF algorithm. Use 'brunet' as default. Available methods for NMF decompositions are 'brunet', 'lee', 'ls-nmf', 'nsNMF', 'offset'. |
optimize |
if |
pynmf |
if |
use_conda |
if |
py_path |
path to Python executable file, e.g. '/Users/wsx/anaconda3/bin/python'. In my
test, it is more stable than |
seed |
specification of the starting point or seeding method, which will compute a starting point, usually using data from the target matrix in order to provide a good guess. |
... |
other arguments passed to |
Value
a list
with Signature
class.
Author(s)
Shixiang Wang
References
Gaujoux, Renaud, and Cathal Seoighe. "A flexible R package for nonnegative matrix factorization." BMC bioinformatics 11.1 (2010): 367.
Mayakonda, Anand, et al. "Maftools: efficient and comprehensive analysis of somatic variants in cancer." Genome research 28.11 (2018): 1747-1756.
See Also
sig_tally for getting variation matrix, sig_estimate for estimating signature number for sig_extract, sig_auto_extract for extracting signatures using automatic relevance determination technique.
Examples
load(system.file("extdata", "toy_copynumber_tally_W.RData",
package = "sigminer", mustWork = TRUE
))
# Extract copy number signatures
res <- sig_extract(cn_tally_W$nmf_matrix, 2, nrun = 1)