filterWindows: Filtering methods for RangedSummarizedExperiment objects

In LTLA/csaw: ChIP-Seq Analysis with Windows

Filtering methods for RangedSummarizedExperiment objects

Description

Convenience functions to compute filter statistics for windows, based on proportions or using enrichment over background.

Usage

filterWindowsGlobal(data, background, assay.data="counts", 
    assay.back="counts", prior.count=2, grid.pts=21)

filterWindowsLocal(data, background, assay.data="counts", 
    assay.back="counts", prior.count=2)

filterWindowsControl(data, background, assay.data="counts", 
    assay.back="counts", prior.count=2, scale.info=NULL)

filterWindowsProportion(data, assay.data="counts", prior.count=2)

scaleControlFilter(data.bin, back.bin, assay.data="counts", 
    assay.back="counts")

Arguments

data	A RangedSummarizedExperiment object containing window-level counts.
background	A RangedSummarizedExperiment object to be used for estimating background enrichment. For filterWindowsGlobal, this should contain counts for large contiguous bins across the genome, for the same samples used to construct data. For filterWindowsLocal, this should contain counts for regions in which rowRanges(data) is nested, for the same samples used to construct data. For filterWindowsControl, this should contain count for the same regions as rowRanges(data) for control samples.
assay.data	A string or integer scalar specifying the assay containing window/bin counts in data.
assay.back	A string or integer scalar specifying the assay containing window/bin counts in background.
prior.count	A numeric scalar, specifying the prior count to use in scaledAverage.
scale.info	A list containing the output of scaleControlFilter, i.e., a normalization factor and library sizes for ChIP and control samples.
data.bin	A RangedSummarizedExperiment containing bin-level counts for ChIP libraries.
back.bin	A RangedSummarizedExperiment containing bin-level counts for control libraries.
grid.pts	An integer scalar specifying the number of grid points to use for interpolation when data contains variable-width intervals, e.g., for peaks or other regions instead of windows.

Details

The aim of these functions is to compute a filter statistic for each window, according to various abundance-based definitions that are discussed below. Windows can then be filtered to retain those with large filter statistics. This selects for high-abundance windows that are more likely to contain genuine binding sites and thus are more interesting for downstream (differential binding) analyses.

Value

All filtering functions return a named list containing:

• abundances, a numeric vector containing the average abundance of each row in data.

• filter, a numeric vector containing the filter statistic for the given type for each row. The definition of this filter statistic will vary across the different methods.

• back.abundances, a numeric vector containing the background abundance for each row in data. Not reported for filterWindowsProportion.

For scaleControlFilter, a named list is returned containing:

• scale, a numeric scalar containing the scaling factor for multiplying the control counts.

• data.totals, a numeric vector containing the library sizes for data.

• back.totals, anumeric vector containing the library sizes for background.

Proportion-based filtering

filterWindowsProportion supposes that a certain percentage of the genome (by length) is genuinely bound. The filter statistic is defined as the ratio of the rank in abundance to the total number of windows. Rank is in ascending order, i.e., higher abundance windows have higher ratios. Windows are retained that have rank ratios above a threshold, e.g., 0.99 if 1% of the genome is assumed to be bound.

The definition of the rank is dependent on the total number of windows in the genome. However, empty windows or bins are automatically discarded in windowCounts (exacerbated if filter is set above unity). This will result in underestimation of the rank or overestimation of the global background. To avoid this, the total number of windows is inferred from the spacing.

Global background filtering

filterWindowsGlobal uses the median average abundance across the genome as a global estimate of the background abundance. This assumes that background contains unfiltered counts for large (2 - 10 kbp) genomic bins, from which the background abundance can be stably computed. The filter statistic for each window is defined as the difference between the window abundance and the global background, after adjusting for the differences in widths between windows and bins.

Similarly to filterWindowsProportion, the total number of bins is inferred from their width in background. This avoids overestimating the global background if some filtering has already been applied to the bins.

The calculation is fairly straightforward for window-level counts where all rowRanges(data) are of the same width. If the widths are variable (e.g., because data contains counts for peaks, genes or other irregular features), the adjustment for differences in width needs to performed separately for each unique width. If there are more than grid.pts unique widths, we expedite this process by computing the adjustment for grid.pts widths and interpolating to obtain the adjusted background at each width.

If background is not supplied, the background abundance is directly computed from entries in data. This assumes that data contains windows for most of the regions in the genome, and that the coverage is sufficiently high across most windows to obtain a stable background estimate.

Local background filtering

filterWindowsLocal compares the abundance of each window to the flanking regions. This considers each window to contain the entirety of a binding event, where any coverage of the surrounding regions is treated as background enrichment. It is analogous to the behaviour of peak-calling methods and accounts for local fluctuations in background, e.g., due to differences in mappability, sequenceability or accessibility.

We assume that each region in data is nested within each corresponding region of background. The counts of each row in data are then subtracted from those of the corresponding row in background. The average abundance of the remaining counts is computed and used as the background abundance. The filter statistic is defined by subtracting the background abundance from the corresponding window abundance for each row.

To generate background, we suggest using regionCounts on a resized rowRanges(data) - see Examples.

Control-based filtering

In filterWindowsControl, we assume that data contains window-level counts for ChIP samples, while background contains counts for the same windows in the control samples. (However, it is also possible to supply nested regions as described in filterWindowsLocal, where each interval in background includes the flanking regions around the corresponding entry in data.) For each window in data, the background abundance is defined as the average abundance of the corresponding row in background. The filter statistic is defined as the difference between the window's average abundance and its background abundance.

Composition biases are likely to be present between ChIP and control samples, where increased binding at some loci reduces coverage of other loci in the ChIP samples. This incorrectly results in smaller filter statistics for the latter loci, as the fold-change over the input is reduced. To correct for this, a normalization factor between ChIP and control samples can be computed with scaleControlFilter and passed to filterWindowsControl using the scale.info argument. A warning is raised if scale.info=NULL.

To use scaleControlFilter, users should supply two RangedSummarizedExperiment objects, each containing the counts for large (~10 kbp) bins in the ChIP and control samples. The difference in the average abundance between the two objects is computed for each bin. The median of the differences across all bins is used as a normalization factor to correct the filter statistics for each window. The assumption is that most bins represent background regions, such that a systematic difference in abundance between ChIP and control should represent the composition bias.

scaleControlFilter will also store the library sizes for each object in its output. This is used to check that data and background have the same library sizes. Otherwise, the normalization factor computed with bin-level counts cannot be correctly applied to the window-level counts.

Details on filter statistic calculations

When computing the filter statistic in background-based methods the abundances of bins/regions in background must be rescaled for comparison to those of smaller windows - see getWidths and scaledAverage for more details. In particular, the effective width of the window is often larger than the width used in windowCounts due to the counting of fragments rather than reads. The fragment length is extracted from data$ext and background$ext, though users will need to set data$rlen or background$rlen for unextended reads (i.e., ext=NA).

The prior.count protects against inflated log-fold increases when the background counts are near zero. A low prior is sufficient if background has large counts, which is usually the case for wide regions. Otherwise, if the set of windows with large filter statistics are dominated by low counts, prior.count should be increased to a larger value like 5.

See Also

windowCounts, aveLogCPM, getWidths, scaledAverage

Examples

bamFiles <- system.file("exdata", c("rep1.bam", "rep2.bam"), package="csaw")
data <- windowCounts(bamFiles, filter=1)

# Proportion-based (keeping top 1%)
stats <- filterWindowsProportion(data)
head(stats$filter)
keep <- stats$filter > 0.99 
new.data <- data[keep,]

# Global background-based (keeping fold-change above 3).
background <- windowCounts(bamFiles, bin=TRUE, width=300)
stats <- filterWindowsGlobal(data, background)
head(stats$filter)
keep <- stats$filter > log2(3)

# Local background-based.
locality <- regionCounts(bamFiles, resize(rowRanges(data), fix="center", 300))
stats <- filterWindowsLocal(data, locality)
head(stats$filter)
keep <- stats$filter > log2(3)

# Control-based, with binning for normalization (pretend rep2.bam is a control).
binned <- windowCounts(bamFiles, width=10000, bin=TRUE)
chip.bin <- binned[,1]
con.bin <- binned[,2]
scinfo <- scaleControlFilter(chip.bin, con.bin)

chip.data <- data[,1]
con.data <- data[,2]
stats <- filterWindowsControl(chip.data, con.data,
    prior.count=5, scale.info=scinfo)

head(stats$filter)
keep <- stats$filter > log2(3)

LTLA/csaw documentation built on Dec. 11, 2023, 5:11 a.m.