clusterAlignment: Data Structure for a collection of all pairwise alignments of...

In flagme: Analysis of Metabolomics GC/MS Data

Description

Store the raw data and optionally, information regarding signal peaks for a number of GCMS runs

Usage

clusterAlignment(pD, runs=1:length(pD@rawdata), timedf=NULL,
               usePeaks=TRUE, verbose=TRUE, ...)

Arguments

pD	a peaksDataset object.
runs	vector of integers giving the samples to calculate set of pairwise alignments over.
timedf	list (length = the number of pairwise alignments) of matrices giving the expected time differences expected at each pair of peaks used with usePeaks=TRUE, passed to peaksAlignment
usePeaks	logical, TRUE uses peakdata list, FALSE uses rawdata list for computing similarity.
verbose	logical, whether to print out info.
...	other arguments passed to peaksAlignment

Details

clusterAlignment computes the set of pairwise alignments.

Value

clusterAlignment object

Author(s)

Mark Robinson, Riccardo Romoli

References

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

See Also

peaksDataset, peaksAlignment

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])

ca <- clusterAlignment(pd, gap=0.5, D=0.05, df=30, metric=1, type=1)

flagme documentation built on Nov. 8, 2020, 5:24 p.m.