mergeWindowsList {csaw} | R Documentation

## Consolidate window sizes

### Description

Consolidate windows of different sizes into a common set of regions.

### Usage

    mergeWindowsList(ranges.list, tol, ...)

    findOverlapsList(ranges.list, regions, ...)

### Arguments

`ranges.list` | A list of RangedSummarizedExperiment and/or GRanges objects.
`tol` | An integer scalar specifying the tolerance to use in `mergeWindows`.
`...` | For `mergeWindowsList`, further arguments to pass to `mergeWindows`. For `findOverlapsList`, further arguments to pass to `findOverlaps`.
`regions` | A GRanges object specifying reference regions of interest for overlapping with windows.

### Details

DB analyses with csaw can be repeated using windows of different sizes to provide comprehensive detection of DB at a range of spatial resolutions. This function merges together those sets of windows of differing sizes, typically generated by multiple calls to `windowCounts` using different `width` values. For `mergeWindowsList`, windows of all sizes are clustered together through `mergeWindows`. For `findOverlapsList`, windows of all sizes are overlapped with `ref` using `findOverlaps`.

The aim is to pass the output of this function to `combineTests` or `combineOverlaps`. This takes the statistical machinery used to combine p-values across windows for a cluster or reference region, and recycles it to provide a rigorous method for consolidating statistics across multiple analyses with different window sizes. However, it requires some care to balance the contribution of analyses with different window sizes to the combined p-value. Otherwise, analyses with many small windows will dominate the calculation for each cluster or reference region.

To compensate for this effect, both functions weight each window's contribution to a cluster or region. The weight is inversely proportional to the number of windows of the same size in the same cluster or region. For `mergeWindowsList`, this yields a weight for each window, while for `findOverlapsList`, this yields a weight for each overlap. Application of those weights in `combineTests` and related functions ensures that the combined p-value is not dominated by numerous small windows within a cluster.

### Value

`mergeWindowsList` returns a named list containing:

`ranges`:

  A GRanges object containing the concatenated intervals from all elements of `x`. The element-wise metadata of this GRanges contains `origin`, an integer field specifying the index of `x` from which each interval was obtained.

`ids`:

  An integer vector of length equal to `ranges`. This specifies the cluster (i.e., entry of `merged`) to which each interval in `ranges` was assigned.

`regions`:

  A GRanges object containing the genomic coordinates of the clusters of merged windows, as computed by `mergeWindows`.

`weights`:

  An numeric vector of length equal to `ranges`, specifying the weight of each interval.

`findOverlapsList` returns a named list containing:

`ranges`:

  A GRanges object containing the concatenated intervals from all elements of `x`. The element-wise metadata of this object contains the integer field `origin`, as described above.

`olap`:

  A Hits object containing the overlaps between `ref` (query) and `ranges` (subject).

`weights`:

  A numeric vector of length equal to `olap`, containing the weight of each overlap.

### Author(s)

Aaron Lun

### See Also

`mergeWindows`, for the functionality behind `mergeWindowsList`.

`findOverlaps`, for the functionality behind `findOverlapsList`.

### Examples

    bamFiles <- system.file("exdata", c("rep1.bam", "rep2.bam"), package="csaw")
    low <- windowCounts(bamFiles, width=1, filter=1)
    med <- windowCounts(bamFiles, width=100, filter=1)
    high <- windowCounts(bamFiles, width=500, filter=1)

    cons <- mergeWindowsList(list(low, med, high), tol=100)
    cons$ranges
    cons$merged

    of.interest <- GRanges(c('chrA', 'chrA', 'chrB', 'chrC'),
        IRanges(c(1, 500, 100, 1000), c(200, 1000, 700, 1500)))
    cons <- findOverlapsList(list(low, med, high), regions=of.interest)
    cons$ranges
    cons$olap

[Package csaw version 1.24.3 Index]