R: compute Optimal Parameters

computeOptimal {ChIPanalyser}

R Documentation

compute Optimal Parameters

Description

ChIPanalyser contains a set of functions some of which require two parameters known as ScalingFactorPWM and as boundMolecules. These two paramters are not always known. computeOptimal will compute these values by maximising the correlation and minimising the Mean Squared Error between a predicted ChIP-seq-like profile and a real ChIP-seq profile for a given loci.

Usage

computeOptimal(DNASequenceSet, genomicProfileParameters, LocusProfile,
    setSequence, DNAAccessibility = NULL,
    occupancyProfileParameters = NULL, parameter = "all",
    peakMethod="moving_kernel",cores=1)

Arguments

`DNASequenceSet`	`DNASequenceSet` is a `DNAStringSet` or a `BSgenome` of the full sequence of the organism of interest.
`genomicProfileParameters`	`genomicProfileParameters` is a `genomicProfileParameters` object containing at least a Postion Frequency Matrix or a Position Weight Matrix. It is strongly advised to customize this object to increase goodness of fit of the model when compared to real ChIP-seq data.
`LocusProfile`	`LocusProfile` is a named list containing ChIP-seq enrichements for each Loci of interest. This Profile should be normalised to a base pair level. In other words, there should be an enrichement score for each base pair of a given Locus.
`setSequence`	`setSequence` is a `GRanges` containing the Loci of interest.
`DNAAccessibility`	`DNAAccessibility` is a `GRanges` object conatining Accesible DNA sites.
`occupancyProfileParameters`	`occupancyProfileParameters` is a `occupancyProfileParameters` object. If this object is not provided (occupancyProfileParameters = NULL), a new object will be created internally. However, it is strongly advised to tailor this object to maximise the goodness of fit of the model when compared to ChIP-seq data.
`parameter`	`parameter` is a character string which determines which parameter should be computed. There are four options c("correlation", "MSE", "theta", "all")
`peakMethod`	`peakMethod` is a character string of one of the following: c("moving_kernel","truncated_kernel","exact"). If set to moving_kernel, the peaks will be approximated using Rcpp (Default). If set to truncated_kernel, the peaks will be approximated however this method does not require Rcpp. If set to exact, the peaks will not be approximated.
`cores`	`cores` is the number cores that will be used to compute optimal set of parameters.

Details

In order to backward infer the values of ScalingFactorPWM and boundMolecules, it is possible to use the computeOptimal to find these parameters. It should be noted that this functions requires a ChIP-seq data input. LocusProfile (ChIP-seq data) should be a named list with normalised ChIP-seq to a single base pair level. Naming should stay consitent with all other names and should represent the names of the loci of interest. The naming procedure should be similar in setSequence. Each range within the GRanges should be named (not to be confused with seqnames )

Value

computeOptimal returns a list respectivly described as the optimal set of Parameters (lambda or ScalingFactorPWM and boundMolecules), the optimal matrix (a matrix containing accuracy estimates dependant on the parameter chosen), and finally the chosen parameter. If the parameter that was chosen was "all", then each element of this list will contain the optimal set of parameters, optimal matricies for "correlation", "Mean Squared Error" and "theta".

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)){
    source("https://bioconductor.org/biocLite.R")
    biocLite("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 Optimal set of Parameters
optimalParam <- computeOptimal(DNASequenceSet = DNASequenceSet,
    genomicProfileParameters = GPP,
    LocusProfile = eveLocusChip,
    setSequence = eveLocus,
    DNAAccessibility = Access,
    occupancyProfileParameters = OPP,
    parameter = "all",
    peakMethod="moving_kernel")

[Package ChIPanalyser version 1.2.0 Index]