CoRe.Binarize_Matrix: Binarize Quantitative Dependency Matrix

In DepMap-Analytics/CoRe: CoRe R package

Binarize Quantitative Dependency Matrix

Description

Binarazation of Quantitative Dependency Matrix.

Usage

CoRe.Binarize_Matrix(depMat,
                            method=c('fdr', 'thr'),
                            ess_genes=NULL,
                            noness_genes=NULL,
                            scaled=FALSE,
                            Bayes_Factor=FALSE,
                            FDRth=0.05,
                            threshold=NULL)

Arguments

depMat	Quantitative Dependency Matrix containing Pan-cancer or tissue/cancer-types specific gene fitness/dependency scores across cell-lines/samples. The value in position [i,j] of such matrix quantifies the fitness/dependency score of the i-th gene in the j-th cell line.
method	A character string indicating which method is to be used for the computation of the binary matrix. One of "fdr" or "thr". - fdr: For every cell line, the function computes the threshold at fixed FDR computed on the two distributions derived from sets of reference essential and non-essential genes. Genes with a logFC score less than the FDR threshold will be converted to 1, 0 otherwise. Different percentages of FDR can be specified by the user, according to the level of stringency (default is 0.05). - thr: For every cell line, any gene with a logFC score less than threshold will be converted to 1, 0 otherwise.
ess_genes	Vector of gene symbols to be used as reference essential genes. Ignored if scaled is set to FALSE.
noness_genes	Vector of gene symbols to be used as reference non-essential genes. Ignored if scaled is set to FALSE.
scaled	Boolean, default is FALSE. Should the Quantitative Dependency matrix be scaled using reference set of essential and non essential genes (provided in input), as detailed in [2]. We always recommend scaling as a normalization procedure.
Bayes_Factor	Boolean, default is FALSE. It indicates whether the Quantitative Dependency matrix was obtained from BAGEL2 [3].
FDRth	a numerical value > 0 and < 1 specifying the false discovery rate threshold at which the matrix binarization will be computed, using two reference sets of essential and non-essential genes.
threshold	default is NULL. A numerical value specifying a hard logFC score threshold. For every cell line, any gene with a logFC score less than threshold will be converted to 1, 0 otherwise. Scaling is warmly recommended for this method, in order to normalize essentiality profiles across cell lines.

Details

This function implements several strategies for the binarization of a quantitative Dependency Matrix.

Value

A binarized version of the Dependency Matrix where rows are genes and columns are cell lines. The entry in position [i,j] indicates whether the i-th gene is essential for the viability of the j-th cell line.

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] Meyers RM, Bryan JG, McFarland JM, et al. Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat Genet. 2017 Dec;49(12):1779-1784. doi: 10.1038/ng.3984.

[3] Eiru Kim & Traver Hart. Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier. Genome Medicine 13, 2 (2021). doi: 10.1186/s13073-020-00809-3.

Examples

## loading reference set of essential/non-essential genes
data(curated_BAGEL_essential)
data(curated_BAGEL_nonEssential)

## Downloading and scaling Quantitative Dependency Matrix
## for > 300 cancer cell lines from [1]
depMat<-CoRe.download_DepMatrix()

## Binarization of logFC matrix using the FDR method, and
## scaling using sets of reference essential/non-essential genes
BinDepMat<-CoRe.Binarize_Matrix(depMat,
                                method='fdr',
                                ess_genes=curated_BAGEL_essential,
                                noness_genes=curated_BAGEL_nonEssential,
                                scaled=TRUE)
head(BinDepMat)

## Binarization of logFC matrix using the threshold method, and
## scaling using sets of reference essential/non-essential genes
BinDepMat<-CoRe.Binarize_Matrix(depMat,
                                method='thr',
                                ess_genes=curated_BAGEL_essential,
                                noness_genes=curated_BAGEL_nonEssential,
                                scaled=TRUE,
                                threshold = -0.5)
head(BinDepMat)

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