Description Usage Arguments Value See Also Examples
View source: R/global_processing.R
Given a set of peak calls for different marks and samples, normalize all peaks genome-wide for each sample and mark by rescaling and Winsorizing, i.e. rescale the middle of the data to the range [0, 1] and bound the upper tail to 1 and the lower tail to 0, effectively replacing a fixed amount of extreme values in each tail. Similar to trimming the tails except instead of discarding the tails entirely they're bounded.
1 | normalizePeaks(peaks, columns, tail = 0.005)
|
peaks |
List of GRanges objects storing peak calls for each sample, where element names correspond to sample IDs |
columns |
Character vector specifying the names of columns to normalize |
tail |
Optional: numeric, a fraction in [0, 1] specifying how much of the data to bound to 0 (for the lower tail) or 1 (for the upper tail). Default: 0.005. |
A list of GRanges objects storing peak calls for each sample, with
columns specified in columns
normalized.
winsorNorm
1 | normalizePeaks(H3K4me3, columns = c("signalValue", "pValue", "qValue"))
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