Resample data frame using values from the column with number of clonesets. Number of clonestes (i.e., rows of a MiTCR data frame) are reads (usually the "Read.count" column) or UMIs (i.e., barcodes, usually the "Umi.count" column).
Data frame with the column
Number of values / reads / UMIs to choose.
Which column choose to represent quanitites of clonotypes. See "Details".
resample. Using multinomial distribution, compute the number of occurences for each cloneset, than remove zero-number clonotypes and
return resulting data frame. Probabilities for
rmultinom for each cloneset is a percentage of this cloneset in
.col column. It's a some sort of simulation of how clonotypes are chosen from the organisms. For now it's not working
very well, so use
.n clones (not clonotypes!) from the input repertoires without any probabilistic simulation, but
exactly computing each choosed clones. Its output is same as for
resample (repertoires), but is more consistent and
Data frame with
sum(.data[, .col]) == .n.
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