tronco.kfold.posterr

A reconstructed model (the output of tronco.capri)

The names of the selected regularizers (bic, aic or caprese)

models

events

a positive integer number, the number of times cross-validation will be run

runs

a positive integer number, the number of groups into which the data will be split

Percentage of cores to use. coresRate * (numCores - 1)

cores.ratio

A parameter to disable/enable verbose messages.

silent


Perform a k-fold cross-validation using the function bn.cv
and scan every node to estimate its posterior classification error.



The TRONCO (TRanslational ONCOlogy) R package collects algorithms to infer
progression models via the approach of Suppes-Bayes Causal Network, both
from an ensemble of tumors (cross-sectional samples) and within an individual
patient (multi-region or single-cell samples). The package provides parallel
implementation of algorithms that process binary matrices where each row
represents a tumor sample and each column a single-nucleotide or a structural
variant driving the  progression; a 0/1 value models the absence/presence of
that alteration in the sample. The tool can import data from plain, MAF or
GISTIC format files, and can fetch it from the cBioPortal for cancer genomics.
Functions for  data manipulation and visualization are provided, as well as
functions to import/export such data to other bioinformatics  tools for, e.g,
clustering or detection of mutually exclusive alterations. Inferred models can be
visualized and tested for their confidence via bootstrap and cross-validation.
TRONCO is used for  the implementation of the Pipeline for Cancer Inference.

Giulio Caravagna 
Alex Graudenzi alexgraudenzidiscounimibit
Daniele Ramazzotti danieleramazzottidiscounimibit

TRONCO


An R package for the inference of cancer progression models from heterogeneous genomic data

TRONCO, an R package for TRanslational ONCOlogy

tronco.kfold.posterr function

Perform a k-fold cross-validation using the function bn.cv
and scan every node to estimate its posterior classification error.

tronco.kfold.posterr: tronco.kfold.posterr

Description

Usage

Arguments

Examples