CoRe.CS_ADaM: Execute ADaM on a specific tissue/cancer type

View source: R/CoRe.R

CoRe.CS_ADaMR 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.

See Also

CoRe.ADaM

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.