Aggregates data by genomic bins
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(data.frame or matrix) Locus-wise values. Rows correspond to genomic intervals (probes, genes, etc.,) while columns correspond to individual samples
(data.frame or GRanges) Locus coordinates. Row order must match
(GRanges) Target intervals, with coordinate system matching that of featureData.
(logical) when TRUE, returns the coordinates of the bin to which each row belongs. Does not aggregate data in any way. This output can be used as input for more complex functions with data from each bin.
(logical) when TRUE, does not aggregate or expect xpr. Only returns number of overlapping subject ranges per bin. Speeds up computation.
(function) function to aggregate data in bin
(logical) set to TRUE to compute mean pairwise
sample correlation (Pearson correlation) for each bin; when TRUE,
this function overrides
(logical) print status messages
Computed mean value of binned data. This function assumes
that all elements in
featureData have identical width.
If provided with elements of disparate widths, the respective widths
are not weighted averaging. This behaviour may change in future
versions of IdeoViz.
This function allows the user to bin data if this hasn't already
been done, and is a step involved in preparing the data for
plotOnIdeo(). This function computes binned within-sample
average of probes overlapping the same range. Where a range
overlaps multiple bins, it gets counted in all.
(GRanges) Binned data or binning statistics; information
returned for non-empty bins only.
The default for this function is to return binned data; alternately,
function will return statistics on bin counts. The latter may be
useful to plot spatial density of the input metric.
The flags and output types are presented in order of evaluation precedence:
getBinCountOnly=TRUE, returns a list with a single
entry: bin_ID: (data.frame) bin information: chrom, start,
end, width, strand, index, and count. "index" is the row number of
target_GR to which this bin corresponds
getBinCountOnly=FALSE, returns a list with three entries:
bin_ID: same as bin_ID in output 1 above
xpr:(data.frame) B-by-n columns where B is total number of [target_GR, featureData] overlaps (see next entry, binmap_idx) and n is number of columns in xpr; column order matches xpr. Contains sample-wise data "flattened" so that each [target,subject] pair is presented. More formally, entry [i,j] contains expression for overlap of row i from binmap_idx for sample j (where 1 <= i <= B, 1 <= j <= n)
binmap_idx:(matrix) two-column matrix:
1) target_GR row, 2) row of featureData which overlaps with index
in column 1. (matrix output of
getBinCountOnly=FALSE, returns a GRanges object. Results are contained in the
elementMetadata slot. For a dataset with n samples, the table would have (n+1) columns; the first column is bin_count, and indicates number of units contained in that bin. Columns (2:(n+1)) contain binned values for each sample in column order corresponding to that of xpr.
doSampleCor=TRUE, result is in a metadata column with name "mean_pairwise"cor". Bins with a single datapoint per sample get a value of NA.
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ideo_hg19 <- getIdeo("hg19") data(GSM733664_broadPeaks) chrom_bins <- getBins(c("chr1","chr2","chrX"), ideo_hg19,stepSize=5*100*1000) # default binning mean_peak <- avgByBin(data.frame(value=GSM733664_broadPeaks[,7]), GSM733664_broadPeaks[,1:3], chrom_bins) # custom function median_peak <- avgByBin( data.frame(value=GSM733664_broadPeaks[,7]), GSM733664_broadPeaks[,1:3], chrom_bins, FUN=median) # mean pairwise sample correlation data(binned_multiSeries) bins2 <- getBins(c("chr1"), ideo_hg19, stepSize=5e6) samplecor <- avgByBin(mcols(binned_multiSeries)[,1:3], binned_multiSeries, bins2, doSampleCor=TRUE) # just get bin count binstats <- avgByBin(data.frame(value=GSM733664_broadPeaks[,7]), GSM733664_broadPeaks[,1:3], chrom_bins, getBinCountOnly=TRUE)
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