dataExtractor {PrecisionTrialDrawer} | R Documentation

## dataExtractor: extract data from a CancerPanel object

### Description

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

### Usage

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

### Arguments

`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

### 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.

### Value

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

### Author(s)

Giorgio Melloni , Alessandro Guida

### See Also

`getAlterations` `subsetAlterations`

### Examples

    # 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)
    ))

[Package PrecisionTrialDrawer version 1.8.0 Index]