CoRe.truePositiveRate: Profile of True Positive Rates

In DepMap-Analytics/CoRe: CoRe R package

Profile of True Positive Rates

Description

This function is used by the ADaM method [1] calculates a profile of True Positive Rates for fitness genes in at least n cell lines, with positive cases from a reference set of essential genes.

Usage

CoRe.truePositiveRate(depMat,
                      essentialGeneSet)

Arguments

depMat	Binary dependency matrix, rows are genes and columns are samples. 1 in position [i,j] indicates that inactivation of the i-th gene exerts a significant loss of fitness in the j-th sample, i.e. the i-th gene is a fitness gene for the j-th cell line, 0 otherwise.
essentialGeneSet	Reference set of predefined essential genes. This is used to define positive cases.

Details

This function calculates true positive rates for fitness genes in at least n cell lines (for each n). First, this function calculates the number of cell lines for which each gene is a fitness gene. Second, for a given number of cell lines, the set of genes that are fitness genes in at least that number of cell lines is determined. Finally, this set of genes is then compared to the reference set of essential genes to calculate a true positive rate.

Value

A list of the following vectors:

P	Vector of number of genes that are fitness genes in a given number of cell lines.
TP	Vector of number of genes that are fitness genes in a given number of cell lines and are true positives, i.e. in the reference set of essential genes provided in input.
TPR	TP divided by number of genes in set reference set of essential genes provided in input.

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.

[2] Dwane L, Behan FM, Gonçalves E, Lightfoot H, Yang W, van der Meer D, Shepherd R, Pignatelli M, Iorio F, Garnett MJ. Project Score database: a resource for investigating cancer cell dependencies and prioritizing therapeutic targets. Nucleic Acids Res. 2021 Jan 8;49(D1):D1365-D1372.

[3] Hart T, Chandrashekhar M, Aregger M, Steinhart Z, Brown KR, MacLeod G, Mis M, Zimmermann M, Fradet-Turcotte A, Sun S, Mero P, Dirks P, Sidhu S, Roth FP, Rissland OS, Durocher D, Angers S, Moffat J. High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities. Cell. 2015 Dec 3;163(6):1515-26. doi: 10.1016/j.cell.2015.11.015. Epub 2015 Nov 25. PMID: 26627737.

Examples

## Downloading binary dependency matrix
## for > 300 cancer cell lines from Project Score [1,2]
BinDepMat<-CoRe.download_BinaryDepMatrix()

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

## Loading a reference set of essential genes from
## from the CRISPRcleanR package, derived from [3]
data(BAGEL_essential)

TPR<-CoRe.truePositiveRate(LungDepMat,BAGEL_essential)

## inspect results
TPR

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