sw.perm.test: Permutation Test for Smith-Waterman Algorithm

In cgh: Microarray CGH analysis using the Smith-Waterman algorithm

Description

Perform a permutation test of island scores from the Smith-Waterman algorithm.

Usage

  sw.perm.test(x, max.nIslands = 1, nIter = 1000, seed = NULL, trace = FALSE)
  

Arguments

x	a vector of real values
max.nIslands	number of iterations of the algorithm, each iteration finding the next highest-scoring 'island' of positive values, or NULL to find all islands
nIter	number of permutations of the input data used in the test
seed	seed for the random number generator, or NULL to use a faster random number generator that cannot be seeded
trace	print verbose output if TRUE

Value

A vector of probability values, calculated as the proportion of instances for which performing the Smith-Waterman algorithm on random permutations of the data identifies a higher-scoring island than the islands identified when the algorithm is performed on the original data

Author(s)

T.S.Price

References

Price TS, et al. SW-ARRAY: a dynamic programming solution for the identification of copy-number changes in genomic DNA using array comparative genome hybridization data. Nucl Acids Res. 2005;33(11):3455-3464.

See Also

sw

Examples

## simluate vector of logratios
set.seed(3)
logratio <- c(rnorm(20) - 1, rnorm(20))

## invert sign of values and subtract threshold to ensure negative mean
x <- sw.threshold(logratio, function(x) median(x) + .2 * mad(x), -1)

## perform Smith-Waterman
sw(x)

## perform permutation test on the islands identified
sw.perm.test(x, max.nIslands = NULL, nIter= 1e4)
  

cgh documentation built on May 2, 2019, 5:51 a.m.