findGuideHits: Calculate guide-level statistics for a single screen

In klarman-cell-observatory/MAUDE: Mean Alterations Using Discrete Expression

Description

Given a table of counts per guide/bin and a bin model for an experiment, calculate the optimal mean expression for each guide

Usage

findGuideHits(countTable, curBinBounds, pseudocount = 10,
  meanFunction = mean, sortBins = c("A", "B", "C", "D", "E", "F"),
  unsortedBin = "NS", nonTargeting = "NT")

Arguments

countTable	a table containing one column for each bin (A-F) and another column for non-targeting guide (logical-"NT"), and unsorted abundance (NS)
curBinBounds	a bin model as created by makeBinModel
pseudocount	the count to be added to each bin count, per 1e6 reads/bin total (default=10 pseudo reads per 1e6 reads total)
meanFunction	how to calculate the mean of the non-targeting guides for centering Z-scores. Defaults to 'mean'
sortBins	the names in countTable of the sorting bins. Defaults to c("A","B","C","D","E","F")
unsortedBin	the name in countTable of the unsorted bin. Defaults to "NS"
nonTargeting	the name in countTable containing a logical representing whether or not the guide is non-Targeting (i.e. a negative control guide). Defaults to "NT"

Value

a data.frame containing the guide-level statistics, including the Z score 'Z', log likelihood ratio 'llRatio', and estimated mean expression 'mean'.

Examples

guideLevelStats = findGuideHits(binReadMat, binBounds)

klarman-cell-observatory/MAUDE documentation built on Nov. 4, 2019, 3:53 p.m.