plotOptimalHeatMaps: Heat Map of optimal Parameters
In ChIPanalyser: ChIPanalyser: Predicting Transcription Factor Binding Sites

Description Usage Arguments Details Value Author(s) References Examples

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

1	plotOptimalHeatMaps(optimalParam,contour=TRUE,col=NULL,main=NULL,layout=TRUE,overlay=FALSE)

`optimalParam`	`optimalParam` is a list containing containing optimal matricies (or only one if only one paramter was selected). These matricies are the result of the `computeOptimal` function
`contour`	`parameter` is logical. Should contour lines be plotted?
`col`	`col` vector of colours to be used for each heat map. If none are specified, rainbow colours will be used. NOTE: colour vector will be recyled if not enough colours are provided.
`main`	`main` title.
`layout`	`layout` is either TRUE or FALSE specifying if stadard layout should be used or not. If TRUE, each heat map will be plotted on an individual page with a heat map scale of the right side.
`overlay`	`overlay` is either TRUE or FALSE specifying if an overlay plot should be produced. The overlay plot takes the top 10 percent of best performing parameters per scoring metric and overlays them in a single plot. The resulting plots shows the optimal set of paramters for all metrics combined.

Once the optimal set of Parameters ( lambdaPWM and boundMolecules ), it is possible to plot the results in the form of a heat map. Each heat map will be plotted in a seperate page if layout = TRUE, If layout= FALSE, it is up to the user to define how they wish to layout there heat maps.

Returns a heat map of optimal combinations of lambdaPWM and boundMolecules. The x axis represents the different value assigned to lambda ( lambdaPWM ) and the y axis represents the different values to boundMolecules ( boundMolecules ).

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 data objects
GPP <- genomicProfiles(PFM=PFM,BPFrequency=DNASequenceSet)

#Computing Optimal set of Parameters
optimalParam <- computeOptimal(genomicProfiles = GPP,
    DNASequenceSet = DNASequenceSet,
    ChIPScore = eveLocusChip,
    chromatinState = Access,
    parameterOptions = OPP,
    parameter = "all",
    peakMethod="moving_kernel")
plotOptimalHeatMaps(optimalParam)