computeChIPProfile: Computing ChIP-seq like profiles from Occupancy data.
In ChIPanalyser: ChIPanalyser: Predicting Transcription Factor Binding Sites

Description Usage Arguments Details Value Author(s) References Examples

computeChIPProfile compute ChIP-seq like profile from occupancy data. Occupancy data is computed using computeOccupancy.

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computeChIPProfile(genomicProfiles, loci, parameterOptions = NULL,
    norm = TRUE, method = c("moving_kernel","truncated_kernel","exact"),
    peakSignificantThreshold= NULL,cores=1, verbose = TRUE)

`genomicProfiles`	`genomicProfiles` is the result of `computeOccupancy`. This object should be a `genomicProfiles` object.
`loci`	`loci` is either a `GRanges` or `ChIPScore` object. ChIPScore-class will be the result of `processingChIP`. This object represents the set of Loci you are interested in analysing. If you have followed the full ChIPanalyser pipe line, you would have used the processingChIP function that would return a ChIPScore-class object containing your loci of interest. GRanges are also supported if you are only using part of the pipeline.
`parameterOptions`	`parameterOptions` is a `parameterOptions` object. This object is used to store the numerous paramters offered by ChIPanalyser. This argument is optional as all arguments are also parse in both ChIPScore-class and genomicProfiles objects. If you wanted to make some last minute changes, `parameterOptions` is the way to go. We recomend that you set your desired options before hand.
`norm`	`norm` is a logical value. If TRUE, the ChIP-seq like profile will be normalised towards maximum Occupancy. If FALSE, the profile will be left as is.
`method`	`method` 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.
`peakSignificantThreshold`	`peakSignificantThreshold` is a threshold at which peaks will be selected. IMPORTANT: if you select "moving_kernel" as described in `method` then this threshold is a numeric value describing the peak tail hight cutoff value (Default = 0.001). In the case of "truncated_kernel" and "exact", the threhsold represents a distance in base pair from the peak summit at which the peak should be cut (Default = 1250). The default is set to NULL in this function. This just means that either the value is provided bu user with the appropriate method. If not, the default will be selected depending on the method selected.
`cores`	`cores` is the number of cores that will be used to compute ChIP profiles.
`verbose`	`verbose` is a logical value. If TRUE, progress messages will be displayed in console. If FALSE, no progress messages will be dispalyed in console.

computeChIPProfile converts Transcription Factor occuapncy to a profile resembling the one of a ChIP-seq profile. Internally a few paramters are required to build a ChIP like profile. These parameters are either defined and stored in a ChIPScore object (Paramters are updated based on your ChIP data ), a genomicProfiles (user defined at the start of the analysis) or a parameterOptions (if you want to update values as you go along)

Returns a genomicProfiles objec containing all ChIP-seq like profile for every combination of lambdaPWM and boundMolecules provided by the user.

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.

#Extracting Data
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 genomicProfiles object
GPP <- genomicProfiles(PFM=PFM, PFMFormat="raw",BPFrequency=DNASequenceSet)


# Computing Genome Wide
GenomeWide <- computeGenomeWideScores(genomicProfiles = GPP,
    DNASequenceSet = DNASequenceSet)

#Compute PWM Scores
PWMScores <- computePWMScore(genomicProfiles = GenomeWide,
     DNASequenceSet = DNASequenceSet, loci = eveLocus, chromatinState = Access)
#Compute Occupnacy
Occupancy <- computeOccupancy(genomicProfiles = PWMScores)

#Compute ChIP profiles
chipProfile <- computeChIPProfile(genomicProfiles=Occupancy,loci=eveLocus)
chipProfile