progressiveAlignment: Data Structure for progressive alignment of many GCMS samples
In flagme: Analysis of Metabolomics GC/MS Data

Description Usage Arguments Details Value Author(s) References See Also Examples

Performs a progressive peak alignment (clustalw style) of multiple GCMS peak lists

1 2	progressiveAlignment(pD, cA, D=50, gap=.5, verbose=TRUE, usePeaks=TRUE, df=30, compress=TRUE, type=2)

`pD`	a `peaksDataset` object
`cA`	a `clusterAlignment` object
`D`	retention time penalty
`gap`	gap parameter
`verbose`	logical, whether to print information
`usePeaks`	logical, whether to use peaks (if `TRUE`) or the full 2D profile alignment (if `FALSE`)
`df`	distance from diagonal to calculate similarity
`compress`	logical, whether to store the similarity matrices in sparse form
`type`	numeric, two different type of alignment function

The progressive peak alignment we implemented here for multiple GCMS peak lists is analogous to how clustalw takes a set of pairwise sequence alignments and progressively builds a multiple alignment. More details can be found in the reference below.

progressiveAlignment object

Mark Robinson

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

peaksDataset, multipleAlignment

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=.5, D=.05, df=30, metric=1, type=1)
pa <- progressiveAlignment(pd, ca, gap=.6, D=.1, df=30, type=1)