plotOptimalHeatMaps {ChIPanalyser} | R Documentation |
plotOptimalHeatMaps
will plot heat maps of optimal
Parameters and highlight the optimal combination of
ScalingFactorPWM
and boundMolecules
plotOptimalHeatMaps(optimalParam, parameter = "all", Contour = TRUE)
optimalParam |
|
parameter |
|
Contour |
|
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.
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".
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, 84–94.
#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)){ 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")