plotOptimalHeatMaps {ChIPanalyser} | R Documentation

## Heat Map of optimal Parameters

### Description

`plotOptimalHeatMaps` will plot heat maps of optimal Parameters and highlight the optimal combination of `ScalingFactorPWM` and `boundMolecules`

### Usage

    plotOptimalHeatMaps(optimalParam, parameter = "all", Contour = TRUE)

### Arguments

`optimalParam` | `optimalParam` is a list resulting of `computeOptimal`. This list should contain three elements: optimal Parameters, optimal Matrix and parameter.
`parameter` | `parameter` is a character string representing which parameter should be plotted. There are four possibilities: "correlation","MSE","theta" and "all"
`Contour` | `Contour` is a logical value indicating if contour lines should be plotted.

### Details

Once the optimal set of Parameters ( `ScalingFactorPWM` and `boundMolecules` ), it is possible to plot the results in the form of a heat map. There are four possible heat maps: a "correlation" heat map that will only show the correlation between a predicted Profile and true ChIP-seq profiles for each combination of Parameters ; "MSE" will show a similar heat map only with the Mean Squared Error associated to each predicted profile, true profile and parameter combination; "theta" is a in house metric describing the best fit between high correlation and low MSE (see `thetaThreshold`); finally "all" will plot all of the above.

### Value

Returns a heat map of optimal combinations of `ScalingFactorPWM` and `boundMolecules`. The x axis represents the different value assigned to lambda ( `ScalingFactorPWM` ) and the y axis represents the different values to boundMolecules ( `boundMolecules` ). The hilighted box describes the optimal combination of parameters to either maximise "correlation" and "theta" or to minimise "MSE".

### 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")
    plotOptimalHeatMaps(optimalParam, parameter="all")

[Package ChIPanalyser version 1.2.0 Index]