CoRe.tradeoffEO_TPR: Calculate ADaM threshold

In DepMap-Analytics/CoRe: CoRe R package

Calculate ADaM threshold

Description

This function is used by the ADaM method [1] to identify the minimum number of cell lines in which a gene needs to be fitness in order to be called core-fitness for all the cell lines analysed by ADaM. This is defined as the n providing the best trade-off between i) coverage of priori-known essential genes in the resulting set of predicted core-fitness genes, i.e. fitness in at least n cell lines, and ii) deviance from expectation of the number of fitness genes in at least n cell lines.

Usage

CoRe.tradeoffEO_TPR(EO,
                    TPR,
                    test_set_name,
                    display = TRUE)

Arguments

EO	Profile of empirical odds values. Computed with the CoRe.empiricalOdds function.
TPR	Profile of True positive rates across number of cell line. Computed with the CoRe.truePositiveRate function.
test_set_name	Name to give to the analysis, used for plotting titles.
display	Boolean, default is TRUE. Should ADaM plots be produced.

Details

Compare and plot the curve of log10 odds ratios with the true positive rate curve, across all tested n value, to find the cross over point.

Value

ADAM model threshold:

point

Number of cell lines for which a gene needs to be a significant fitness gene in order to be predicted as core-fitness gene.

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.

See Also

CoRe.empiricalOdds, CoRe.truePositiveRate CoRe.ADaM

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)

# Generate the profiles of number of fitness genes across number of cell lines from
# observed data and corresponding comulative sums, plotting the results
pprofile<-CoRe.panessprofile(depMat=LungDepMat)

# Generate a set of random profiles of number of genes depleted for a number of cell lines
# and corresponding cumulative sums by perturbing observed data.
nullmodel<-CoRe.generateNullModel(depMat=LungDepMat,ntrials = 1000)

# Calculate log10 odd ratios of observed/expected profiles of cumulative number of fitness
# genes in fixed number of cell lines.
EO<-CoRe.empiricalOdds(observedCumSum = pprofile$CUMsums,simulatedCumSum =nullmodel$nullCumSUM )

# Calculate True positive rates for fitness genes in at least n cell lines in the observed
# dependency matrix, with positive cases from a reference set of essential genes
TPR<-CoRe.truePositiveRate(LungDepMat,BAGEL_essential)

# Calculate minimum number of cell lines a gene needs to be a fitness gene in order to
# be considered as a core-fitness gene
crossoverpoint<-CoRe.tradeoffEO_TPR(EO,TPR$TPR,test_set_name = 'BAGEL essential')
crossoverpoint

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