CoRe.CF_Benchmark | R Documentation |
This function assesses the set of predicted core fitness genes by computing the recall (and other ROC indicators) of prior known essential genes and false positives.
CoRe.CF_Benchmark(testedGenes, background, priorKnownSignatures, falsePositives, displayBar=TRUE)
testedGenes |
Vector of gene symbols that have been identified as tissue-specific or Pan-cancer core fitness genes. |
background |
Vector of gene symbols included in the Dependency Matrix used to make the prediction (the background population). |
priorKnownSignatures |
A List of string vectors containg each a signature of prior known essential genes (their symbol)[1]. |
falsePositives |
Genes to be used to compute false positive rates, this can be for example lowly expressed genes from the CCLE [2], assembled through the |
displayBar |
Boolean, default is TRUE. Should a heatmap of the signatures' coverage be plotted. |
Computes recall and other ROC indicators for identified core fitness genes with respect to pre-defined signatures of essential and false positive genes defined in input.
TPRs |
Dataframe listing Recall and enrichment p-values (obtained from hypergeometric distribution) associated with each signature of prior known essential genes. |
PPV |
Positive predicted value obtained pooling all inputed signatures together and using them as positive cases. |
FPR |
False positive rate of the inputed false positive genes. |
C. Pacini, E. Karakoc, A. Vinceti & F. Iorio
[1] Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102:15545.
[2] Barretina, J., Caponigro, G., Stransky, N. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
[3] Behan FM, Iorio F, Picco G, Gonçalves E, et al. Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature. 2019 Apr;568(7753):511-516.
[4] 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.
CoRe.AssembleFPs
# Benchmarking the identified PanCancer Core fitness genes against # prior known essential genes [1] # loading signtures of prior known essential genes data(EssGenes.DNA_REPLICATION_cons) data(EssGenes.HISTONES) data(EssGenes.KEGG_rna_polymerase) data(EssGenes.PROTEASOME_cons) data(EssGenes.SPLICEOSOME_cons) data(EssGenes.ribosomalProteins) data(curated_BAGEL_essential) signatures<-list(DNA_REPLICATION=EssGenes.DNA_REPLICATION_cons, HISTONES=EssGenes.HISTONES, RNA_POLYMERASE=EssGenes.KEGG_rna_polymerase, PROTEASOME=EssGenes.PROTEASOME_cons, SPLICEOSOME=EssGenes.SPLICEOSOME_cons, RIBOSOMAL_PROTS=EssGenes.ribosomalProteins) # downloading binary dependency matrix from project Score [3] BinDepMat<-CoRe.download_BinaryDepMatrix() ## Running ADaM [3] to identify Pan-Cancer core fitness genes ## defining the cell line tissues to be used in the first step of ADaM tissues_ctypes<-c("Haematopoietic and Lymphoid", "Ovary", "Peripheral Nervous System", "Central Nervous System", "Pancreas", "Head and Neck", "Bone", "Lung", "Large Intestine", "Esophagus", "Endometrium", "Stomach", "Breast") ## Downloading cell line model annotations from the Cell Model Passports [3] clannotation<- CoRe.download_AnnotationModel( 'https://cog.sanger.ac.uk/cmp/download/model_list_latest.csv.gz') ## Running ADaM [2] PanCancer_CF_genes<- CoRe.PanCancer_ADaM(pancan_depMat = BinDepMat, tissues_ctypes = tissues_ctypes, clannotation = clannotation, TruePositives = curated_BAGEL_essential, display = FALSE) ## Assemling lowly expressed genes from the CCLE [2] FPs<-CoRe.AssembleFPs() ## benchmarking the core fitness genes predicted by ADaM ## plotting a heatmap highlighting the recalled prior known essential genes ## with barplots and enrichhment pvalues ADaMperf<-CoRe.CF_Benchmark(PanCancer_CF_genes, background = rownames(BinDepMat),priorKnownSignatures = signatures,falsePositives=FPs) ## Inspecting TPRs, PPV and FPR ADaMperf$TPRs ADaMperf$PPV ADaMperf$FPR
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