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

Description

Perform the Smith-Waterman algorithm on a vector of real values.

Usage

 ```1 2``` ``` sw(x, max.nIslands = NULL, trace = FALSE) ```

Arguments

 `x` a vector of real values `max.nIslands` the number of iterations of the algorithm performed. Each iteration finds the next highest-scoring 'island' of positive values. Set to NULL to find all islands `trace` print verbose output if TRUE

Details

The Smith-Waterman algorithm detects 'islands' of positive scores in a vector of real values. The input values should have a negative mean. The algorithm can be used to identify regions of copy number change in microarray fluorescence logratios, once the logratios have been adjusted for sign and a suitable threshold value subtracted to ensure a negative mean: see `sw.threshold`

Value

 `x` the input vector `s` a numeric vector containing the partial sums after one iteration of the Smith-Waterman algorithm `score` a numeric vector of island scores `start` a numeric vector of indices identifying the start of each island `length` a numeric vector of island lengths

T.S.Price

References

Smith TF, Waterman MS. Identification of common molecular subsequences. J Mol Biol. 1981;147(1):195-7.

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.

`sw.threshold` `sw.perm.test` `sw.rob` `sw.plot`
 ``` 1 2 3 4 5 6 7 8 9 10``` ```## 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), sign = -1) ## perform Smith-Waterman algorithm sw(x, trace = TRUE) ```