MS Data

multipleAlignment-class

R Documentation

Data Structure for multiple alignment of many GCMS samples

Description

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

Usage

multipleAlignment(
  pd,
  group,
  bw.gap = 0.8,
  wn.gap = 0.6,
  bw.D = 0.2,
  wn.D = 0.05,
  filterMin = 1,
  lite = FALSE,
  usePeaks = TRUE,
  df = 50,
  verbose = TRUE,
  timeAdjust = FALSE,
  doImpute = FALSE,
  metric = 2,
  type = 2,
  penality = 0.2,
  compress = FALSE
)

Arguments

`pd`	a `peaksDataset` object
`group`	factor variable of experiment groups, used to guide the alignment algorithm
`bw.gap`	gap parameter for "between" alignments
`wn.gap`	gap parameter for "within" alignments
`bw.D`	distance penalty for "between" alignments. When `type = 2` represent the retention time window expressed in seconds
`wn.D`	distance penalty for "within" alignments. When `type = 2` represent the retention time window expressed in seconds
`filterMin`	minimum number of peaks within a merged peak to be kept in the analysis
`lite`	logical, whether to keep "between" alignment details (default, `FALSE`)
`usePeaks`	logical, whether to use peaks (if `TRUE`) or the full 2D profile alignment (if `FALSE`)
`df`	distance from diagonal to calculate similarity
`verbose`	logical, whether to print information
`timeAdjust`	logical, whether to use the full 2D profile data to estimate retention time drifts (Note: time required)
`doImpute`	logical, whether to impute the location of unmatched peaks
`metric`	numeric, different algorithm to calculate the similarity matrix between two mass spectrum. `metric=1` call `normDotProduct()`; `metric=2` call `ndpRT()`; `metric=3` call `corPrt()`
`type`	numeric, two different type of alignment function
`penality`	penalization applied to the matching between two mass spectra if `(t1-t2)>D`
`compress`	logical whether to compress the similarity matrix into a sparse format.

Details

multipleAlignment is the data structure giving the result of an alignment across several GCMS runs. Multiple alignments are done progressively. First, all samples with the same tg$Group label with be aligned (denoted a "within" alignment). Second, each group will be summarized into a pseudo-data set, essentially a spectrum and retention time for each matched peak of the within-alignment. Third, these "merged peaks" are aligned in the same progressive manner, here called a "between" alignment.

Value

multipleAlignment 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.

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])

## multiple alignment
ma <- multipleAlignment(pd, c(1, 1), wn.gap = 0.5, wn.D = 0.05, bw.gap = 0.6,
	                      bw.D = 0.2, usePeaks = TRUE, filterMin = 1, df = 50,
	                      verbose = TRUE, metric = 1, type = 1)

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