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]