CoRe.CS_ADaM | R Documentation |
Execute ADaM on a tissue- or cancer-type-specific binary dependency submatrix.
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)
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 |
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. |
Execute sequentially the whole ADaM pipeline on a tissue or cancer type specific dependency submatrix.
coreFitnessGenes |
A vector of strings with estimated Core Fitness Genes' symbols for the tissue/cancer type of interest. |
C. Pacini, E. Karakoc, A. Vinceti & F. Iorio
[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.
CoRe.ADaM
## 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)
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