The legacy CAGEset class

In CAGEr: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining

options(signif = 3, digits = 3)
knitr::opts_chunk$set(tidy = FALSE, cache = TRUE, autodep = TRUE, fig.height = 5.5,
                      message = FALSE, error = FALSE, warning = TRUE)
set.seed(0xdada)

This document reproduces the commands used in older versions of the CAGEr vignette that were based on the legacy CAGEset class. Its purpose is to check that the results of the computations are identical with the old documentation and with the current documentation based on the new CAGEexp class.

library(CAGEr)
library(BSgenome.Drerio.UCSC.danRer7)

inputDir <- system.file("extdata", package = "CAGEr")
pathsToInputFiles <- list.files(inputDir, pattern = "ctss", full.names = TRUE)
basename(pathsToInputFiles)

myCAGEset <- new("CAGEset", genomeName = "BSgenome.Drerio.UCSC.danRer7",
  inputFiles = pathsToInputFiles, inputFilesType = "ctss",
  sampleLabels = c("zf_30p_dome", "zf_high", 
  "zf_prim6_rep1", "zf_prim6_rep2", "zf_unfertilized_egg"))
myCAGEset
TSS.df <- read.table(system.file("extdata/Zf.unfertilized.egg.chr17.ctss", 
  package = "CAGEr"))
colnames(TSS.df) <- c("chr", "pos", "strand", "zf_unfertilized_egg")
TSS.df$chr <- as.character(TSS.df$chr)
TSS.df$pos <- as.integer(TSS.df$pos)
TSS.df$strand <- as.character(TSS.df$strand)
TSS.df$zf_unfertilized_egg <- as.integer(TSS.df$zf_unfertilized_egg)
head(TSS.df)

myCAGEset.coerced <- as(TSS.df, "CAGEset")
myCAGEset.coerced

getCTSS(myCAGEset)
ctss <- CTSStagCount(myCAGEset)
head(ctss)
sampleLabels(myCAGEset)

corr.m <- plotCorrelation(myCAGEset, samples = "all", method = "pearson")

mergeSamples(myCAGEset, mergeIndex = c(3,2,4,4,1), 
  mergedSampleLabels = c("zf_unfertilized_egg", 
  "zf_high", "zf_30p_dome", "zf_prim6"))

librarySizes(myCAGEset)
plotReverseCumulatives(myCAGEset, fitInRange = c(5, 1000), onePlot = TRUE)
normalizeTagCount(myCAGEset, method = "powerLaw", 
  fitInRange = c(5, 1000), alpha = 1.2, T = 5*10^4)
exportCTSStoBedGraph(myCAGEset, values = "normalized", 
  format = "bedGraph", oneFile = TRUE)
exportCTSStoBedGraph(myCAGEset, values = "normalized", format = "BigWig")

clusterCTSS(object = myCAGEset, threshold = 1, thresholdIsTpm = TRUE, 
  nrPassThreshold = 1, method = "distclu", maxDist = 20, 
  removeSingletons = TRUE, keepSingletonsAbove = 5)
tc <- tagClusters(myCAGEset, sample = "zf_unfertilized_egg")
head(tc)

cumulativeCTSSdistribution(myCAGEset, clusters = "tagClusters")
quantilePositions(myCAGEset, clusters = "tagClusters", qLow = 0.1, qUp = 0.9)
tc <- tagClusters(myCAGEset, sample = "zf_unfertilized_egg",
  returnInterquantileWidth = TRUE,  qLow = 0.1, qUp = 0.9)
head(tc)
exportToBed(object = myCAGEset, what = "tagClusters", 
  qLow = 0.1, qUp = 0.9, oneFile = TRUE)
plotInterquantileWidth(myCAGEset, clusters = "tagClusters", 
  tpmThreshold = 3, qLow = 0.1, qUp = 0.9)

aggregateTagClusters(myCAGEset, tpmThreshold = 5, 
  qLow = 0.1, qUp = 0.9, maxDist = 100)
consensusCl <- consensusClusters(myCAGEset)
head(consensusCl)
cumulativeCTSSdistribution(myCAGEset, clusters = "consensusClusters",
  useMulticore = TRUE)
quantilePositions(myCAGEset, clusters = "consensusClusters", qLow = 0.1,
  qUp = 0.9, useMulticore = TRUE)
consensusCl <- consensusClusters(myCAGEset, sample = "zf_unfertilized_egg",
  returnInterquantileWidth = TRUE,  qLow = 0.1, qUp = 0.9)
head(consensusCl)

getExpressionProfiles(myCAGEset, what = "consensusClusters", tpmThreshold = 10, 
  nrPassThreshold = 1, method = "som", xDim = 4, yDim = 2)
plotExpressionProfiles(myCAGEset, what = "consensusClusters")
class3_1 <- extractExpressionClass(myCAGEset, 
  what = "consensusClusters", which = "3_1")
head(class3_1)

exportToBed(myCAGEset, what = "consensusClusters", 
  colorByExpressionProfile = TRUE)
cumulativeCTSSdistribution(myCAGEset, clusters = "consensusClusters")

scoreShift(myCAGEset, groupX = "zf_unfertilized_egg", groupY = "zf_prim6",
  testKS = TRUE, useTpmKS = FALSE)
shifting.promoters <- getShiftingPromoters(myCAGEset, 
  tpmThreshold = 5, scoreThreshold = 0.6,
  fdrThreshold = 0.01)
head(shifting.promoters)

sessionInfo()

CAGEr documentation built on Jan. 17, 2021, 2 a.m.