getTilingElementwiseStats: Find active elements by sliding window

In Carldeboer/MAUDE: Mean Alterations Using Discrete Expression

Find active elements by sliding window

Description

Tests guides for activity by considering a sliding window across the tested region and including all guides within the window for the test.

Usage

getTilingElementwiseStats(
  experiments,
  normNBSummaries,
  tails = "both",
  location = "pos",
  chr = "chr",
  window = 500,
  minGuides = 5,
  negativeControl = "NT",
  ...
)

Arguments

experiments	a data.frame containing the headers that demarcate the screen ID, which are all also present in normNBSummaries
normNBSummaries	data.frame of guide-level statistics as generated by findGuideHits()
tails	whether to test for increased expression ("upper"), decreased ("lower"), or both ("both"); (defaults to "both")
location	the name of the column in normNBSummaries containing the chromosomal location (defaults to "pos")
chr	the name of the column in normNBSummaries containing the chromosome name (defaults to "chr")
window	the window width in base pairs (defaults to 500)
minGuides	the minimum number of guides in a window required for a test (defaults to 5)
negativeControl	the name in normNBSummaries containing a logical representing whether or not the guide is non-Targeting (i.e. a negative control guide). Defaults to "NT"
...	other parameters for getZScalesWithNTGuides

Value

a data.frame containing the statistics for all windows tested for activity

Examples

fakeReadData = data.frame(id=rep(1:10000,2), expt=c(rep("e1",10000), rep("e2",10000)), 
                          A=rpois(20000, lambda = 100), B=rpois(20000, lambda = 100),
                          C=rpois(20000, lambda = 100), D=rpois(20000, lambda = 100),
                          E=rpois(20000, lambda = 100), F=rpois(20000, lambda = 100),
                          NotSorted=rpois(20000, lambda = 100), 
                          position = rep(c(rep(NA, 1000), (1:9000)*10 + 5E7),2), 
                          chr=rep(c(rep(NA, 1000), rep("chr1", 9000)),2), 
                          negControl = rep(c(rep(TRUE,1000),rep(FALSE,9000)),2), 
                          stringsAsFactors = FALSE)
#make one region an "enhancer" and "repressor" by skewing the reads 
enhancers = data.frame(name = c("enh","repr"), start = c(40000, 70000) + 5E7, 
  end = c(40500, 70500) + 5E7, chr="chr1")
enhancerData = findOverlappingElements(fakeReadData[!is.na(fakeReadData$position),], enhancers, 
  guides.pos = "position",elements.start = "start", elements.end = "end")
readSkew=1.2 # we will scale up/down the reads in ABC and DEF by this amount
enhancerData[enhancerData$name=="enh", c("D","E","F")] = 
  floor(readSkew* enhancerData[enhancerData$name=="enh", c("D","E","F")]);
enhancerData[enhancerData$name=="repr", c("D","E","F")] = 
  floor(enhancerData[enhancerData$name=="repr", c("D","E","F")]/readSkew);
enhancerData[enhancerData$name=="repr", c("A","B","C")] = 
  floor(readSkew* enhancerData[enhancerData$name=="repr", c("A","B","C")]);
enhancerData[enhancerData$name=="enh", c("A","B","C")] = 
  floor(enhancerData[enhancerData$name=="enh", c("A","B","C")]/readSkew);
#replace the original data for these elements
fakeReadData = rbind(fakeReadData[!(fakeReadData$id %in% enhancerData$id), ],
  enhancerData[names(fakeReadData)])
#make experiments and sorting strategy
expts = unique(fakeReadData["expt"]);
curSortBins = makeBinModel(data.frame(Bin = c("A","B","C","D","E","F"), fraction = rep(0.1,6)))
curSortBins = rbind(curSortBins, curSortBins) # duplicate and use same for both expts
curSortBins$expt = c(rep(expts$expt[1],6),rep(expts$expt[2],6))
guideHits = findGuideHitsAllScreens(experiments = expts, countDataFrame=fakeReadData,
                                    binStats = curSortBins, unsortedBin = "NotSorted",
                                    negativeControl="negControl")
if(require("ggplot2")){
  p=ggplot(guideHits, aes(x=position, y=Z))+geom_point() ; print(p)
}
tilingElementStats = getTilingElementwiseStats(experiments = expts, normNBSummaries = guideHits, 
  tails = "both", chr = "chr", location="position", negativeControl = "negControl")
if(require("ggplot2")){
  p=ggplot(tilingElementStats, aes(x=start, xend=end, y=significanceZ, yend=significanceZ, 
    colour=FDR<0.01))+geom_segment() ; print(p)
}

Carldeboer/MAUDE documentation built on March 27, 2022, 8:50 p.m.