bootstrap.tcr: Bootstrap for data frames in package tcR.

In tcR: Advanced Data Analysis of Immune Receptor Repertoires

Description

Resample rows (i.e., clones) in the given data frame and apply the given function to them.

Usage

bootstrap.tcr(
  .data,
  .fun = entropy.seg,
  .n = 1000,
  .size = nrow(.data),
  .sim = c("uniform", "percentage"),
  .postfun = function(x) {     unlist(x) },
  .verbose = T,
  .prop.col = "Read.proportion",
  ...
)

Arguments

.data	Data frame.
.fun	Function applied to each sample.
.n	Number of iterations (i.e., size of a resulting distribution).
.size	Size of samples. For .sim == "uniform" stands for number of rows to take. For .sim == "percentage" stands for number of UMIs / read counts to take.
.sim	A character string indicating the type of simulation required. Possible values are "uniform" or "percentage". See "Details" for more details of type of simulation.
.postfun	Function applied to the resulting list: list of results from each processed sample.
.verbose	if T then show progress bar.
.prop.col	Column with proportions for each clonotype.
...	Further values passed to .fun.

Details

Argument .sim can take two possible values: "uniform" (for uniform distribution), when each row can be taken with equal probability, and "perccentage" when each row can be taken with probability equal to its "Read.proportion" column.

Value

Either result from .postfun or list of length .n with values of .fun.

Examples

## Not run: 
# Apply entropy.seg function to samples of size 20000 from immdata$B data frame for 100 iterations.
bootstrap.tcr(immdata[[2]], .fun = entropy.seg, .n = 100, .size = 20000, .sim = 'uniform')

## End(Not run)

tcR documentation built on July 2, 2020, 3:18 a.m.