run.strong.peaks: Locate Peaks that are "Large" in All Samples

Description

Takes the file generated by run.peaks, extracts all peaks that are “large” in all samples, and writes the results to a file.

Usage

run.strong.peaks(cor.thresh = 0.8, isotope.dist = 7, pre.align = FALSE, align.method = c("PL", "spline", "affine", "none"), align.fcn = NA, root.dir = ".", lrg.dir,  lrg.file = "lrg_peaks.RData", overwrite = FALSE,  use.par.file = FALSE, par.file = "parameters.RData")

Arguments

cor.thresh

threshold correlation for declaring isotopes

isotope.dist

maximum distance for declaring isotopes

pre.align

either FALSE, or a numeric vector of shifts to apply to spectra, or a three-component list (of the form described in the Note section below) to be used before identifying peaks from different spectra

align.method

alignment algorithm for peaks

align.fcn

function (and inverse) to apply to masses before (and after) applying align.method; see below

root.dir

directory for parameters file and raw data

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

Value

No value returned; the file is simply created.

Details

Reads in information from file created by run.lrg.peaks, locates peaks which appear in all samples, and overwrites the file lrg.file in lrg.dir. The resulting file contains variables

amps

data frame of amplitudes of non-isotope peaks that occur in all samples

centers

data frame of centers of non-isotope peaks that occur in all samples

lrg.peaks

the data frame of significant peaks created by run.lrg.peaks

and is ready to be used by run.cluster.matrix.

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.

Zhang, L.-K. et al. (2005) “Accurate mass measurements by Fourier transform mass spectrometry”. Mass Spectrom Rev, 24:2, 286--309.