extractBinding: Extract binding values for graphical representations
In descostesn/ChIPSeqSpike: ChIP-Seq data scaling according to spike-in control

Description Usage Arguments Details Value Methods (by class) Author(s) See Also Examples

Extracts and formats binding scores for each experiment into structures adapted to performing different graphical representations.

extractBinding(theObject, gff_vec, genome, binsize = 50, before = 2000, 
               after=2000, mean_or_median = "mean", interpolation_number = 100,
               interpolation_average = 10000, ignore_strand = FALSE, 
               verbose = FALSE)
            
## S4 method for signature 'ChIPSeqSpikeDataset'
extractBinding(theObject, gff_vec, genome, 
                binsize = 50, before = 2000, after=2000, 
                mean_or_median = "mean", interpolation_number = 100, 
                interpolation_average = 10000, ignore_strand = FALSE, 
                verbose = FALSE)

## S4 method for signature 'ChIPSeqSpikeDatasetBoost'
extractBinding(theObject, gff_vec, genome,
                binsize = 50, before = 2000, after=2000, 
                mean_or_median = "mean", interpolation_number = 100, 
                interpolation_average = 10000, ignore_strand = FALSE, 
                verbose = FALSE)

## S4 method for signature 'ChIPSeqSpikeDatasetList'
extractBinding(theObject, gff_vec, genome, 
                binsize = 50, before = 2000, after=2000, 
                mean_or_median = "mean", interpolation_number = 100, 
                interpolation_average = 10000, ignore_strand = FALSE, 
                verbose = FALSE)

## S4 method for signature 'ChIPSeqSpikeDatasetListBoost'
extractBinding(theObject, gff_vec, 
                genome, binsize = 50, before = 2000, after=2000, 
                mean_or_median = "mean", interpolation_number = 100, 
                interpolation_average = 10000, ignore_strand = FALSE, 
                verbose = FALSE)

`theObject`	`ChIPSeqSpike` dataset (see ?spikeDataset)
`gff_vec`	File in GFF format containing annotations used to plot information
`genome`	The UCSC code of reference genome, e.g. 'hg19' for Homo sapiens (see details)
`binsize`	Binning size used to create bigwig files. Default is 50.
`before`	Length in bp of the interval upstream annotation. Default is 2000.
`after`	Length in bp of the interval downstream annotation. Default is 2000.
`mean_or_median`	For average profiles, should the 'mean' or 'median' values be used. Default is 'mean'.
`interpolation_number`	Number of interpolated points to create matrices (see details). Default is 100.
`interpolation_average`	Number of interpolated points of profiles and heatmaps (see details). Default is 10000.
`ignore_strand`	If TRUE, the directionality is ignored, that is all features strands, regardless of annotation in GFF file, are treated as undetermined ("*"). Default is FALSE.
`verbose`	If TRUE, output processing messages. Default is FALSE.

This method should be called before performing any graphical analysis. It updates two slots of theObject:

SetArrayList: Contains the binding values to perform meta-profile (see ?plotProfile); transformation profiles if not in boost mode (see ?plotTransform) and heatmaps (see ?plotHeatmaps). These values are stored in a plotSetArray object. This object is created by the method getPlotSetArray of the 'seqplots' package.
matBindingList: Contains list of matrices for each experiment. Each row correspond to an annotation given by gff_vec and the number of columns is defined by the interpolation_number parameter. These matrices are used to perform boxplots (see ?boxplotSpike) and correlation plots (see ?plotCor).

The SetArrayList contains values for 4 kind of representations (profiles and heatmaps): Representation at the start of the annotation (-before/ +after parameters); at the midpoint of the annotation; at the end of the annotation (-before/+after) or at the entire annotation (-before/+after). For representations using the entire annotations and upstream (before)/ downstream intervals, the number of points used for the within annotation interpolation is defined by the interpolation_average parameter.

For details on installing reference genomes, see details of the function 'getPlotSetArray' of the 'seqplots' package.

Returns the same object with binding values in the form of plotSetArray and matrices (see details).

ChIPSeqSpikeDataset: Method for signature theObject= 'ChIPSeqSpikeDataset'
ChIPSeqSpikeDatasetBoost: Method for signature theObject= 'ChIPSeqSpikeDatasetBoost'
ChIPSeqSpikeDatasetList: Method for signature theObject= 'ChIPSeqSpikeDatasetList'
ChIPSeqSpikeDatasetListBoost: Method for signature theObject= 'ChIPSeqSpikeDatasetListBoost'

Nicolas Descostes

spikeDataset plotProfile plotTransform plotHeatmaps boxplotSpike plotCor getPlotSetArray

## Mock example on a restricted number of reads
info_file_csv <- system.file("extdata/info.csv", package="ChIPSeqSpike")
bam_path <- system.file(c("extdata/bam_files"), package="ChIPSeqSpike")
bigwig_path <- system.file(c("extdata/bigwig_files"), package="ChIPSeqSpike")
gff_vec <- system.file("extdata/test_coord.gff", package="ChIPSeqSpike")
genome_name <- "hg19"

if(.Platform$OS.type != 'windows') {
    csds <- spikeDataset(infoFile = info_file_csv, bamPath = bam_path, 
                         bigWigPath = bigwig_path)

    ## Copying test files to the current folder
    originalBW_vec <- as.character(getBigWigFile(csds))
    dir.create("./test_chipseqspike")
    result <- file.copy(originalBW_vec, "test_chipseqspike")

    csds <- estimateScalingFactors(csds)

    ## Apply RPM scaling
    csds <- scaling(csds, outputFolder = "test_chipseqspike")

    ## Perform input subtraction
    csds <- inputSubtraction(csds)

    ## Reverse RPM scaling after input subtraction
    csds <- scaling(csds, reverse = TRUE)

    ## Apply exogenous scaling factors
    csds <- scaling(csds, type = "exo")

    ## Extract binding values
    csds <- extractBinding(csds, gff_vec, genome_name)

    ## Delete all files generated in this example
    unlink("test_chipseqspike/", recursive = TRUE)
}