R: Computing ChIP-seq like profiles from Occupancy data.

computeChipProfile {ChIPanalyser}

R Documentation

Computing ChIP-seq like profiles from Occupancy data.

Description

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

Usage

computeChipProfile(setSequence, occupancy, occupancyProfileParameters = NULL,
    norm = TRUE, method = c("moving_kernel","truncated_kernel","exact"),
    peakSignificantThreshold= NULL,cores=1, verbose = TRUE)

Arguments

`setSequence`	`setSequence` is a `GRanges` containing the loci of interest. It is strongly advised to name each loci / range in the `GRanges`.
`occupancy`	`occupancy` is a `link{genomicProfileParameters}` object result of the `computeOccupancy` function. This `genomicProfileParameters` object should contain a list, GRanges or a GRangesList with both PWMScore and Occupancy as meta data columns. To check if your object contains the right data, please see `AllSitesAboveThreshold`.
`occupancyProfileParameters`	`occupancyProfileParameters` is an `occupancyProfileParameters` object containing the desired values for each Parameter. If left as NULL, `computeChipProfile` will generate a new `occupancyProfileParameters` object using the default values (see `occupancyProfileParameters`).
`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.

Details

computeChipProfile converts Transcription Factor occuapncy to a profile resembling the one of a ChIP-seq profile. A certain set of Parameters are required in order to build ChIP profiles. These Parameters are defined and storedin a occupancyProfileParameters object. These parameters are: chipMean, chipSd, chipSmooth, stepSize,backgroundSignal, maxSignal and removeBackground. All these Parameters have default values already stored. However, for an optimal fit, it is advised to derive these values from actual ChIP-seq data. For more information on these parameters, see occupancyProfileParameters. This functions also requires a set of sequencesin form of a GRanges. The sequence set are the loci of interest on which the ChIP-seq profile will be computed.

Value

Returns a list containing all ChIP-seq like profile for every combination of ScalingFactorPWM and boundMolecules. The correlation and Mean Squared Error between the prdicted ChIP profile and actual ChIP-seq profile for the same loci will vary depending on the value given for ScalingFactorPWM

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


#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)){
    source("https://bioconductor.org/biocLite.R")
    biocLite("BSgenome.Dmelanogaster.UCSC.dm3")
    }
library(BSgenome.Dmelanogaster.UCSC.dm3)
DNASequenceSet <- getSeq(BSgenome.Dmelanogaster.UCSC.dm3)
# Building genomicProfileParameters object
GPP <- genomicProfileParameters(PFM=PFM, BPFrequency=DNASequenceSet)

OPP <- occupancyProfileParameters()

# Computing Genome Wide
GenomeWide <- computeGenomeWidePWMScore(DNASequenceSet = DNASequenceSet,
    genomicProfileParameters = GPP)

#Compute PWM Scores
PWMScores <- computePWMScore(DNASequenceSet = DNASequenceSet,
    genomicProfileParameters = GenomeWide,
    setSequence = eveLocus, DNAAccessibility = Access)
#Compute Occupnacy
Occupancy <- computeOccupancy(AllSitesPWMScore = PWMScores,
    occupancyProfileParameters = OPP)

#Compute ChIP profiles
chipProfile <- computeChipProfile(setSequence = eveLocus,
    occupancy = Occupancy, occupancyProfileParameters = OPP)
chipProfile

[Package ChIPanalyser version 1.2.0 Index]