runMeDeCom | R Documentation |
Perform a MeDeCom experiment
runMeDeCom(data, Ks, lambdas, opt.method = "MeDeCom.cppTAfact", cg_subsets = NULL, sample_subset = NULL, startT = NULL, startA = NULL, trueT = NULL, trueA = NULL, fixed_T_cols = NULL, NINIT = 100, ITERMAX = 1000, NFOLDS = 10, N_COMP_LAMBDA = 4, NCORES = 1, random.seed = NULL, num.tol = 1e-08, analysis.name = NULL, use.ff = FALSE, cluster.settings = NULL, temp.dir = NULL, cleanup = TRUE, verbosity = 1L, time.stamps = FALSE)
data |
DNA methylation dataset as a |
Ks |
values of parameter k to be tested, vector of type |
lambdas |
values of parameter λ to be tested, vector of type |
opt.method |
optimization method used. Currently supported values are |
cg_subsets |
a |
sample_subset |
samples to include into the analysis |
startT |
a |
startA |
a |
trueT |
a numeric matrix with as many rows as there are methylation sites in |
trueA |
a numeric matrix with as many columns as there are methylation sites in |
fixed_T_cols |
columsn of T which are known (to be implemented) |
NINIT |
number of random initializations |
ITERMAX |
maximal number of iterations of the alternating optimization scheme |
NFOLDS |
number of cross-validation folds |
N_COMP_LAMBDA |
the number of solutions to compare in the "smoothing" step |
NCORES |
number of cores to be used in the parallelized steps (at best a divisor of NINIT) |
random.seed |
seed for random number generation |
num.tol |
some small parameter |
analysis.name |
a deliberate name of the analysis as a |
use.ff |
use |
cluster.settings |
a list with parameters for an HPC cluster |
temp.dir |
a temporary directory for the cluster-based analysis available on all nodes |
cleanup |
if |
verbosity |
verbosity level, |
time.stamps |
add timestamps to the diagnostic output |
MeDeComSet object
Pavlo Lutsik
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.