Agregate counts across a set of regions, e.g. for footprinting analysis
# S4 method for DsATAC aggregateRegionCounts( .object, regionGr, samples = getSamples(.object), countAggrFun = "sum", norm = "tailMean", normTailW = 0.1, kmerBiasAdj = TRUE, k = 6, sampleCovg = NULL, sampleKmerFreqM = NULL, regionKmerFreqM = NULL, silent = FALSE )
GRanges object specifying the regions to aggregate over
aggration function to be used for summarizing the insertion counts at each position. Possible values include
method used for normalizing the resulting position-wise counts.
'tailMean' is supported, which computes normalization factors as the mean signal in the tails of the window
fraction of the region window to be used on each side of the window to be used for normalization if
norm is one of
compute Tn5 bias and use it to adjust the counts as in Corces, et al., Science, (2018)
length of the kmer to be used for sequence bias correction. Only relevant if
to save compute time, a sample coverage track list (as computed by
getCoverage(.object)) can be supplied. If not, it will be computed on the fly.
to save compute time, a matrix of sample kmer frequency at insertion sites (as computed by
getInsertionKmerFreq(.object, ...)) can be supplied.
If not, it will be computed on the fly. Only relevant if
to save compute time, a matrix of region kmer frequencies (kmers X window width).
Must have the same number of rows as the specified (or computed)
and the same number of columns as the window width (median width of
Only relevant if
limit log messages
data.frame containing position-wise counts (raw, normalized and optionally Tn5-bias-corrected) for each sample