ct.PRC: Generate a Precision-Recall Curve from a CRISPR screen

In OscarBrock/gCrisprTools: Suite of Functions for Pooled Crispr Screen QC and Analysis

Generate a Precision-Recall Curve from a CRISPR screen

Description

Given a set of targets of interest, this function generates a Precision Recall curve from the results of a CRISPR screen. Specifically, it orders the target elements in the screen in the specified direction, and then plots the recall rate (proportion of true targets identified) against the precision (proportion of identified targets that are true targets).

Note that ranking statistics in CRISPR screens are (usually) permutation-based, and so some granularity in the rankings is expected. This function does a little extra work to ensure that hits are counted as soon as the requisite value of the ranking statistic is reached regardless of where the gene is located within the block of equally-significant genes. Functionally, this means that the drawn curve is somewhat anticonservative in cases where the gene ranks are not well differentiated.

Usage

ct.PRC(
  summaryDF,
  target.list,
  direction = c("enrich", "deplete"),
  plot.it = TRUE
)

Arguments

summaryDF	A dataframe summarizing the results of the screen, returned by the function ct.generateResults.
target.list	A character vector containing the names of the targets to be tested; by default these are assumed to be 'geneID's, but specifying 'collapse=geneSymbol' enables setting on 'geneSymbol' by passing that value through to 'ct.simpleResult'.
direction	Direction by which to order target signals ('enrich' or 'deplete').
plot.it	Logical value indicating whether to plot the curves.

Value

A list containing the the x and y coordinates of the curve.

Author(s)

Russell Bainer

Examples

data('resultsDF')
data('essential.genes') #Note that this is an artificial example.
pr <- ct.PRC(resultsDF, essential.genes, 'enrich')
str(pr)

OscarBrock/gCrisprTools documentation built on Oct. 25, 2022, 7:29 a.m.