permutDistTest: Monte Carlo permutation test for pairwise and one-vs-all-wise...

In tcR: Advanced Data Analysis of Immune Receptor Repertoires

Description

WARNING: this is an experimental procedure, work is still in progress.

Perform permutation tests of distances among groups for the given groups of samples and matrix of distances among all samples.

Usage

permutDistTest(
  .mat,
  .groups,
  .n = 1000,
  .fun = mean,
  .signif = 0.05,
  .plot = T,
  .xlab = "Values",
  .title = "Monte Carlo permutation testing of overlaps",
  .hjust = -0.1,
  .vjust = -4
)

Arguments

.mat	Symmetric matrix of repertoire distances.
.groups	Named list with names of repertoires in groups.
.n	Number of permutations for each pair of group.
.fun	A function to apply to distances.
.signif	Significance level. Below this value hypotheses counts as significant.
.plot	If T than plot the output results. Else return them as a data frame.
.xlab	X lab label.
.title	Main title of the plot.
.hjust	Value for adjusting the x coordinate of p-value labels on plots.
.vjust	Value for adjusting the y coordinate of p-value labels on plots.

See Also

repOverlap, intersectClonesets, ozScore, pca2euclid

Examples

## Not run: 
data(twb)
mat <- repOverlap(twb)
permutDistTest(mat, list(tw1 = c('Subj.A', 'Subj.B'), tw2 = c('Subj.C', 'Subj.D')))
permutDistTest(mat, list(tw1 = c('Subj.A', 'Subj.B'), tw2 = c('Subj.C', 'Subj.D')), .fun = median)

## End(Not run)

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