dp: Dynamic programming algorithm, given a similarity matrix

In rromoli/flagme: Analysis of Metabolomics GC/MS Data

Dynamic programming algorithm, given a similarity matrix

Description

This function calls C code for a bare-bones dynamic programming algorithm, finding the best cost path through a similarity matrix.

Usage

dp(M, gap = 0.5, big = 1e+10, verbose = FALSE)

Arguments

M	similarity matrix
gap	penalty for gaps
big	large value used for matrix margins
verbose	logical, whether to print out information

Details

This is a pretty standard implementation of a bare-bones dynamic programming algorithm, with a single gap parameter and allowing only simple jumps through the matrix (up, right or diagonal).

Value

list with element match with the set of pairwise matches.

Author(s)

Mark Robinson

References

Mark D Robinson (2008). Methods for the analysis of gas chromatography - mass spectrometry data PhD dissertation University of Melbourne.

See Also

normDotProduct

Examples

require(gcspikelite)

# paths and files
gcmsPath<-paste(find.package("gcspikelite"),"data",sep="/")
cdfFiles<-dir(gcmsPath,"CDF",full=TRUE)
eluFiles<-dir(gcmsPath,"ELU",full=TRUE)

# read data, peak detection results
pd<-peaksDataset(cdfFiles[1:2],mz=seq(50,550),rtrange=c(7.5,8.5))
pd<-addAMDISPeaks(pd,eluFiles[1:2])

# similarity matrix
r<-normDotProduct(pd@peaksdata[[1]],pd@peaksdata[[2]])

# dynamic-programming-based matching of peaks
v<-dp(r,gap=.5)

rromoli/flagme documentation built on Feb. 10, 2023, 12:59 a.m.