aggregateRegionCounts-DsATAC-method.RdAgregate 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
)DsATAC object
GRanges object specifying the regions to aggregate over
sample identifiers
aggration function to be used for summarizing the insertion counts at each position. Possible values include "sum", "mean", and "median"
method used for normalizing the resulting position-wise counts.
Currently only '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 'tailMean'
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 kmerBiasAdj==TRUE.
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 kmerBiasAdj==TRUE.
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) sampleKmerFreqM (kmers)
and the same number of columns as the window width (median width of regionGr).
Only relevant if kmerBiasAdj==TRUE.
limit log messages
a data.frame containing position-wise counts (raw, normalized and optionally Tn5-bias-corrected) for each sample