CoRe.coreFitnessGenes: Determining Core Fitness from a binary dependency matrix and...

In DepMap-Analytics/CoRe: CoRe R package

Determining Core Fitness from a binary dependency matrix and required minimal number of dependent cell lines.

Description

This function identifies as Core Fitness those genes that are fitness genes in at least n cell lines (defined in input), according to the binary dependency matrix defined in input. This minimal n is estimated through the ADaM method [1] by the CoRe.tradeoffEO_TPR function.

Usage

CoRe.coreFitnessGenes(depMat,
                      crossoverpoint)

Arguments

depMat	Binary dependency matrix where rows are genes and columns are cell-lines/samples. A 1 in position [i,j] indicates that the inactivation of the i-th gene exerts a significant loss of fitness in the j-th sample, i.e. that gene is a fitness gene for that cell line
crossoverpoint	The estimated minimum number of cell lines in which a gene should be a significant fitness gene in order to be called a core-fitness gene.

Value

A vector of string containing the predicted core fitness genes.

Author(s)

C. Pacini, E. Karakoc, A. Vinceti & F. Iorio

References

[1] Behan FM, Iorio F, Picco G, Gonçalves E, Beaver CM, Migliardi G, et al. Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature. 2019;568:511–6.

See Also

CoRe.tradeoffEO_TPR

Examples

## Downloading a binary dependency matrix
## for > 300 cancer cell lines from [1]
BinDepMat<-CoRe.download_BinaryDepMatrix()

## Extracting dependency submatrix for
## Non-Small Cell Lung Carcinoma cell lines only
LungDepMat<-CoRe.extract_tissueType_SubMatrix(BinDepMat)

## Compute as core-fitness genes those that are fitness
## in at least 20 Non-Small Cell Lung Carcinoma cell lines
cfgenes <- CoRe.coreFitnessGenes(depMat=LungDepMat,crossoverpoint=20)

DepMap-Analytics/CoRe documentation built on July 6, 2022, 8:01 a.m.