Read sig_fit_bootstrap for more option setting.

sig_fit_bootstrap_batch(
catalogue_matrix,
methods = c("QP"),
n = 100L,
min_count = 1L,
p_val_thresholds = c(0.05),
use_parallel = FALSE,
seed = 123456L,
job_id = NULL,
result_dir = tempdir(),
...
)

## Arguments

catalogue_matrix a numeric matrix V with row representing components and columns representing samples, typically you can get nmf_matrix from sig_tally() and transpose it by t(). a subset of c("NNLS", "QP", "SA"). the number of bootstrap replicates. minimal exposure in a sample, default is 1. Any patient has total exposure less than this value will be filtered out. a vector of relative exposure threshold for calculating p values. if TRUE, use parallel computation based on furrr package. It can also be an integer for specifying cores. random seed to reproduce the result. a job ID, default is NULL, can be a string. When not NULL, all bootstrapped results will be saved to local machine location defined by result_dir. This is very useful for running more than 10 times for more than 100 samples. see above, default is temp directory defined by R. other common parameters passing to sig_fit_bootstrap, including sig, sig_index, sig_db, db_type, mode, auto_reduce etc.

## Value

a list of data.table.

## Examples

W <- matrix(c(1, 2, 3, 4, 5, 6), ncol = 2)
colnames(W) <- c("sig1", "sig2")
W <- apply(W, 2, function(x) x / sum(x))

H <- matrix(c(2, 5, 3, 6, 1, 9, 1, 2), ncol = 4)
colnames(H) <- paste0("samp", 1:4)

V <- W %*% H
V