profileAccuracyEstimate {ChIPanalyser} | R Documentation

## Estimating Accuracy of predicted Profiles

### Description

`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.

### Usage

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

### Arguments

`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

### Details

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.

### Value

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

### Author(s)

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

### References

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

### Examples

    #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)

[Package ChIPanalyser version 1.9.1 Index]