tronco.gabow: Tronco Gabow

In TRONCO: TRONCO, an R package for TRanslational ONCOlogy

Description

Reconstruct a progression model using Gabow algorithm combined with probabilistic causation. For details and examples regarding the inference process and on the algorithm implemented in the package, we refer to the Vignette Section 6.

Usage

tronco.gabow(
  data,
  regularization = "no_reg",
  score = "pmi",
  do.boot = TRUE,
  nboot = 100,
  pvalue = 0.05,
  min.boot = 3,
  min.stat = TRUE,
  boot.seed = NULL,
  silent = FALSE,
  epos = 0,
  eneg = 0,
  do.raising = TRUE
)

Arguments

data	A TRONCO compliant dataset.
regularization	Select the regularization for the likelihood estimation, e.g., BIC, AIC.
score	Select the score for the estimation of the best tree, e.g., pointwise mutual information (pmi), conditional entropy (entropy).
do.boot	A parameter to disable/enable the estimation of the error rates give the reconstructed model.
nboot	Number of bootstrap sampling (with rejection) to be performed when estimating the selective advantage scores.
pvalue	Pvalue to accept/reject the valid selective advantage relations.
min.boot	Minimum number of bootstrap sampling to be performed.
min.stat	A parameter to disable/enable the minimum number of bootstrap sampling required besides nboot if any sampling is rejected.
boot.seed	Initial seed for the bootstrap random sampling.
silent	A parameter to disable/enable verbose messages.
epos	Error rate of false positive errors.
eneg	Error rate of false negative errors.
do.raising	Whether to use or not the raising condition as a prior.

Value

A TRONCO compliant object with reconstructed model

Examples

data(test_dataset_no_hypos)
recon = tronco.gabow(test_dataset_no_hypos, nboot = 1)

TRONCO documentation built on Nov. 8, 2020, 5:51 p.m.