progressiveAlignment-class: Data Structure for progressive alignment of many GCMS samples

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

Data Structure for progressive alignment of many GCMS samples

Description

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

Usage

progressiveAlignment(
  pD,
  cA,
  D = 50,
  gap = 0.5,
  verbose = TRUE,
  usePeaks = TRUE,
  df = 30,
  compress = FALSE,
  type = 2
)

Arguments

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

Details

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.

Value

progressiveAlignment object

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

peaksDataset, multipleAlignment

Examples

require(gcspikelite)
files <- list.files(path = paste(find.package("gcspikelite"), "data",
                    sep = "/"),"CDF", full = TRUE)
data <- peaksDataset(files[1:2], mz = seq(50, 550), rtrange = c(7.5, 8.5))
## create settings object
mfp <- xcms::MatchedFilterParam(fwhm = 10, snthresh = 5)
cwt <- xcms::CentWaveParam(snthresh = 3, ppm = 3000, peakwidth = c(3, 40),
 prefilter = c(3, 100), fitgauss = FALSE, integrate = 2, noise = 0,
 extendLengthMSW = TRUE, mzCenterFun = "wMean")
data <- addXCMSPeaks(files[1:2], data, settings = mfp)
data
ca <- clusterAlignment(data, gap = 0.5, D = 0.05, df = 30, metric = 1,
  type = 1, compress = FALSE)
pa <- progressiveAlignment(data, ca, gap = 0.6, D = 0.1, df = 30,
 type = 1, compress = FALSE)

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