profileAccuracyEstimate: Estimating Accuracy of predicted Profiles
In ChIPanalyser: ChIPanalyser: Predicting Transcription Factor Binding Sites

Description Usage Arguments Details Value Author(s) References Examples

View source: R/profileAccuracyEstimateDev.R

profileAccuracyEstimate will compare the predicted ChIP-seq-like profile to real ChIP-seq data and return a set of metrics describing how accurate the predicted model is compared to real data.

1 2	profileAccuracyEstimate(genomicProfiles,ChIPScore, parameterOptions=NULL,method="all",cores=1)

`genomicProfiles`	`genomicProfiles` is the result of `computeChIPProfile`
`ChIPScore`	`ChIPScore` is the result of `processingChIP`. Extracted/Normalised experimental ChIP scores.
`parameterOptions`	`parameterOptions` is a `parameterOptions` object for paramter specification.
`method`	`method` is the method that will be used to assess model quality agianst ChIP-seq data. Method can be one of the following: pearson, spearman, kendall, ks, geometric,fscore, MSE,or all.Fscore contains f-score, precision,recall, MCC, Accuracy and AUC ROC.
`cores`	`cores` is the number of cores used to extract ChIP scores. Default = 1

In order to assess the quality of the model against experimental ChIP-seq data, ChIPanalyser offers a wide range of method to choose from. These methods are also used when computing optimal paramters.

Returns list of goodness of fit metrics for each loci and each parameter selected.

Patrick C. N. Martin <pm16057@essex.ac.uk>

Zabet NR, Adryan B (2015) Estimating binding properties of transcription factors from genome-wide binding profiles. Nucleic Acids Res., 43, 84<e2><80><93>94.

#Data extraction
data(ChIPanalyserData)
# path to Position Frequency Matrix
PFM <- file.path(system.file("extdata",package="ChIPanalyser"),"BCDSlx.pfm")
#As an example of genome, this example will run on the Drosophila genome

  if(!require("BSgenome.Dmelanogaster.UCSC.dm3", character.only = TRUE)){
      if (!requireNamespace("BiocManager", quietly=TRUE))
          install.packages("BiocManager")
      BiocManager::install("BSgenome.Dmelanogaster.UCSC.dm3")
      }
  library(BSgenome.Dmelanogaster.UCSC.dm3)
  DNASequenceSet <- getSeq(BSgenome.Dmelanogaster.UCSC.dm3)
  # Building genomicProfiles object
  GPP <- genomicProfiles(PFM=PFM, BPFrequency=DNASequenceSet)


  # Computing Genome Wide
  GenomeWide <- computeGenomeWideScore(genomicProfiles = GPP,
      DNASequenceSet = DNASequenceSet)

  #Compute PWM Scores
  PWMScores <- computePWMScore(genomicProfiles = GenomeWide,
       DNASequenceSet = DNASequenceSet, loci = eveLocus, chromatinState = Access)
  #Compute Occupnacy
  Occupancy <- computeOccupancy(genomicProfiles = PWMScores)

  #Compute ChIP profiles
  chipProfile <- computeChIPProfile(genomicProfiles=Occupancy,loci=eveLocus)
#Estimating accuracy estimate
AccuracyEstimate <- profileAccuracyEstimate(genomicProfiles = chipProfile,
    ChIPScore = eveLocusChip,
    occupancyProfileParameters = OPP)