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(LocusProfile, predictedProfile,
    occupancyProfileParameters = NULL,method="all")

Arguments

LocusProfile

LocusProfile is a list containing normalised ChIP-seq profiles of the Loci of interest (real data). Each is profile is a numeric vector of length equals to the length of the locus in base pair.

predictedProfile

predictedProfile is the result of the computeChipProfile function. Generally, the output of this function comes in the form of a list of GRangesList. Each GRangesList contains a GRanges with the predicted ChIP-seq-like profiles for each Loci of interest.

occupancyProfileParameters

occupancyProfileParameters is a occupancyProfileParameters object

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 or all. All of these will also return the MSE. Fscore contains f-score, precision,recall, MCC, Accuracy and AUC ROC.

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 a list of two elements. The first element represents lists containing the model quality assessments for every combination of parameters (Bound Molecules and lambda) for every genomic region. The second element of the list contains the result of the ROCR package: False positives, False Negative, etc... See ROCR package.

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 data objects
GPP <- genomicProfileParameters(PFM=PFM,BPFrequency=DNASequenceSet)
OPP <- occupancyProfileParameters()

# Computing Genome Wide
GenomeWide <- computeGenomeWidePWMScore(DNASequenceSet = DNASequenceSet,
    genomicProfileParameters = GPP)

#Compute PWM Scores
PWMScores <- computePWMScore(DNASequenceSet = DNASequenceSet,
    genomicProfileParameters = GenomeWide,
    setSequence = eveLocus, DNAAccessibility = Access)
#Compute Occupnacy
Occupancy <- computeOccupancy(AllSitesPWMScore = PWMScores,
    occupancyProfileParameters = OPP)

#Compute ChIP profiles
chipProfile <- computeChipProfile(setSequence = eveLocus,
    occupancy = Occupancy,
    occupancyProfileParameters = OPP)
#Estimating accuracy estimate
AccuracyEstimate <- profileAccuracyEstimate(LocusProfile = eveLocusChip,
    predictedProfile = chipProfile,
    occupancyProfileParameters = OPP)
[Package ChIPanalyser version 1.4.0 Index]