CoRe: CoRe R package

CoRe.CS_ADaM

R Documentation

Execute ADaM on a specific tissue/cancer type

Description

Execute ADaM on a tissue- or cancer-type-specific binary dependency submatrix.

Usage

CoRe.CS_ADaM(pancan_depMat,
                     tissue_ctype = 'Non-Small Cell Lung Carcinoma',
                     clannotation = NULL,
                     display=TRUE,
                     main_suffix='fitness genes in at least 1 cell line',
                     xlab='n. dependent cell lines',
                     ntrials=1000,
                     verbose=TRUE,
                     TruePositives)

Arguments

`pancan_depMat`	Binary Dependency Matrix containing all cell models.
`tissue_ctype`	A string specifying the tissue/cancer type of interest, this must be compliant with the Cell Model Passports annotation [1].
`clannotation`	Cancer cell line models' annotation from the cell model passports. This can be downloaded using the `CoRe.download_AnnotationModel` function
`display`	Boolean, default is TRUE. Should bar plots of dependency profiles and boxplots of estimated empirical distribution be visualised.
`main_suffix`	If display=TRUE, title suffix to be given to plots of number of genes that are essential/fitness in a give number of cell lines, default is 'genes depleted in at least 1 cell line'.
`xlab`	x-axis label of the plots, default is 'n. cell lines'.
`ntrials`	Integer, default =1000. How many times the dependency matrix shouldd be suffled in order to generate null distributions of number of genes that are essential in fixed numbers of cell lines
`verbose`	Boolean, default is TRUE. Should the computation progress be monitored.
`TruePositives`	Vector of gene symbols to be used as prior known essential genes.

Details

Execute sequentially the whole ADaM pipeline on a tissue or cancer type specific dependency submatrix.

Value

coreFitnessGenes

A vector of strings with estimated Core Fitness Genes' symbols for the tissue/cancer type of interest.

Author(s)

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

References

[1] Van der Meer D, Barthorpe S, Yang W, et al. Cell Model Passports-a hub for clinical, genetic and functional datasets of preclinical cancer models. Nucleic Acids Res. 2019;47(D1):D923–D929.

[2] 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.

[3] Hart T, Chandrashekhar M, Aregger M, et al. High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities. Cell. 2015 Dec 3;163(6):1515-26.

Examples

## downloading a reference set of prior known essential genes from [3]
## curated as detailed in [2]

data(curated_BAGEL_essential)

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

## Perform all the analyses but on different tissues or cancer-types
clannotation<-
  CoRe.download_AnnotationModel('https://cog.sanger.ac.uk/cmp/download/model_list_latest.csv.gz') ## dataset from [2]

SNCLC_cf_genes<-CoRe.CS_ADaM(BinDepMat,tissue_ctype = 'Non-Small Cell Lung Carcinoma',
                             clannotation = clannotation,
                             TruePositives = curated_BAGEL_essential)

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