CoRe.CF_Benchmark: Recall of known essential genes and ROC indicators

In DepMap-Analytics/CoRe: CoRe R package

Recall of known essential genes and ROC indicators

Description

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.

Usage

CoRe.CF_Benchmark(testedGenes,
                          background,
                          priorKnownSignatures,
                          falsePositives,
                          displayBar=TRUE)

Arguments

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 CoRe.AssembleFPs function.
displayBar	Boolean, default is TRUE. Should a heatmap of the signatures' coverage be plotted.

Details

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.

Value

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.

Author(s)

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

References

[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.

See Also

CoRe.AssembleFPs

Examples

# 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

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