dataExtractor: dataExtractor: extract data from a CancerPanel object
In PrecisionTrialDrawer: A Tool to Analyze and Design NGS Based Custom Gene Panels

Description Usage Arguments Details Value Author(s) See Also Examples

Given user specified options, return specific data from a CancerPanel object, including alteration and Samples

dataExtractor(object 
    , alterationType=c("copynumber" , "expression" , "mutations" , "fusions")
    , tumor_type=NULL 
    , collapseMutationByGene=TRUE 
    , collapseByGene=FALSE 
    , tumor.weights=NULL)

`object`	An instance of class CancerPanel
`alterationType`	what kind of alteration to include. It can be one or more between "copynumber", "expression", "mutations", "fusions". Default is to include all kind of alterations.
`tumor_type`	only plot one or more tumor types among the ones available in the object.
`collapseMutationByGene`	A logical that collapse all mutations on the same gene for a single patient as a single alteration.
`collapseByGene`	A logical that collapse all alterations on the same gene for a single patient as a single alteration. e.g. if a sample has TP53 both mutated and deleted as copynumber, it will count for one alteration only.
`tumor.weights`	A named vector of integer values containing an amount of samples to be randomly sampled from the data. Each element should correspond to a different tumor type and is named after its tumor code. See details

This function is used internally by most of the methods of the package and provide a common data extractor for a CancerPanel object. It is a low level function to retrieve data for other custom usages, in particular via tumor.weights.

A named list with data, samples and tumors not present in the CancerPanel object is returned.

Giorgio Melloni , Alessandro Guida

getAlterations subsetAlterations

# Retrieve example data
data(cpObj)
# Extract CNA and mutation data
mydata <- dataExtractor(cpObj 
, alterationType=c("copynumber" , "mutations") 
, tumor_type="brca")
# It is particularly useful for bootstrap simulations
# Here we extract 10 random samples composed by 30 brca and 40 luad
myboot <- replicate(10
, dataExtractor(cpObj
, alterationType="mutations"
, tumor.weights=c("brca"=30 , "luad"=40)
))