extractBinding {ChIPSeqSpike} | R Documentation

## Extract binding values for graphical representations

### Description

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

### Usage

    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)

### Arguments

`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.

### Details

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.

### Value

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

### Methods (by class)

- `ChIPSeqSpikeDataset`: Method for signature theObject= 'ChIPSeqSpikeDataset'

- `ChIPSeqSpikeDatasetBoost`: Method for signature theObject= 'ChIPSeqSpikeDatasetBoost'

- `ChIPSeqSpikeDatasetList`: Method for signature theObject= 'ChIPSeqSpikeDatasetList'

- `ChIPSeqSpikeDatasetListBoost`: Method for signature theObject= 'ChIPSeqSpikeDatasetListBoost'

### Author(s)

Nicolas Descostes

### See Also

`spikeDataset` `plotProfile` `plotTransform` `plotHeatmaps` `boxplotSpike` `plotCor` `getPlotSetArray`

### Examples

    ## 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)
    }

[Package ChIPSeqSpike version 1.8.0 Index]