run.lrg.peaks {FTICRMS}R Documentation

Extract "Large" Peaks from Files

Description

Takes the files output by run.peaks, extracts “large” peaks, combines them into a single data frame, and writes the data frame to a file.

Usage

run.lrg.peaks(trans.method = c("shiftedlog", "glog", "none"), 
              add.par = 0, subtract.base = FALSE,
              root.dir = ".", peak.dir, base.dir, lrg.dir,
              lrg.file = lrg_peaks.RData, overwrite = FALSE,
              use.par.file = FALSE, par.file = "parameters.RData",
              calc.all.peaks = FALSE, gengamma.quantiles = TRUE,
              peak.thresh = 3.798194, subs)

Arguments

trans.method type of transformation to use on spectra before statistical analysis
add.par additive parameter for "shiftedlog" or "glog" options for trans.method
subtract.base logical; whether to subtract calculated baseline from spectrum
root.dir directory for parameters file and raw data
peak.dir directory for peak location files; default is paste(root.dir, "/All_Peaks", sep = "")
base.dir directory for baseline files; default is paste(root.dir, "/Baselines", sep = "")
lrg.dir directory for large peaks file; default is paste(root.dir, "/Large_Peaks", sep = "")
lrg.file name of file to store large peaks in
overwrite logical; whether to replace existing files with new ones
use.par.file logical; if TRUE, then parameters are read from par.file in directory root.dir
par.file string containing name of parameters file
calc.all.peaks logical; whether to calculate all possible peaks or only sufficiently large ones
gengamma.quantiles logical; whether to use generalized gamma quantiles when calculating large peaks
peak.thresh threshold for declaring large peak; see below
subs subset of spectra to use for analysis; see below

Details

Reads in information from each file created by run.peaks, extracts peaks which are “large” (see below), and creates the file lrg.file in lrg.dir. The resulting file contains the data frame lrg.peaks, which has columns
Center_hat estimated mass of peak
Max_hat estimated intensity of peak
Width_hat estimated width of peak
File name of file the peak was extracted from, with “_peaks.RData” deleted
and is ready to be used by run.strong.peaks.

Value

No value returned; the file is simply created.

Note

If use.par.file == TRUE and other parameters are entered into the function call, then the parameters entered in the function call overwrite those read in from the file. This is opposite from the behavior for FTICRMS versions 0.7 and earlier.

trans.method can be abbreviated.

If gengamma.quantiles == TRUE, then a peak is “large” if it is at least peak.thresh times as large as the estimated baseline at that point.

If gengamma.quantiles == FALSE, then a peak is “large” if it has zero weight in the data generated by run.peaks for the spectrum it comes from when using Tukey's biweight with parameter K = 1.5 * peak.thresh to estimate center and scale.

If subs is not defined, the algorithm finds large peaks for all files in peak.dir. If it is defined, subs can be logical or numeric or character; if it is defined, then the algorithm finds large peaks for all entries in subs (character) or list.files(peak.dir)[subs] (logical or numeric).

Author(s)

Don Barkauskas (barkda@wald.ucdavis.edu)

References

Barkauskas, D.A. and D.M. Rocke. (2009a) “A general-purpose baseline estimation algorithm for spectroscopic data”. to appear in Analytica Chimica Acta. doi:10.1016/j.aca.2009.10.043

Barkauskas, D.A. et al. (2009b) “Analysis of MALDI FT-ICR mass spectrometry data: A time series approach”. Analytica Chimica Acta, 648:2, 207–214.

Barkauskas, D.A. et al. (2009c) “Detecting glycan cancer biomarkers in serum samples using MALDI FT-ICR mass spectrometry data”. Bioinformatics, 25:2, 251–257.

See Also

run.peaks, run.cluster.matrix


[Package FTICRMS version 0.8 Index]