run.peaks: Locate Potential Peaks in FT-ICR MS Spectra

Description

Takes baseline-corrected data and locates potential peaks in the spectra.

Usage

run.peaks(trans.method = c("shiftedlog", "glog", "none"), add.par = 0, subtract.base = FALSE, root.dir = ".",  base.dir, peak.dir, overwrite = FALSE, use.par.file = FALSE, par.file = "parameters.RData", num.pts = 5, R2.thresh = 0.98, oneside.min = 1, peak.method = c("parabola", "locmaxes"),  calc.all.peaks = FALSE, gengamma.quantiles = TRUE,  peak.thresh = 3.798194)

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

base.dir

directory for baseline files; default is paste(root.dir, "/Baselines", sep = "")

peak.dir

directory for peak location files; default is paste(root.dir, "/All_Peaks", sep = "")

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

num.pts

number of consecutive points needed for peak fitting

R2.thresh

$R^2$ value needed for peak fitting

oneside.min

minimum number of points on each side of local maximum for peak fitting

peak.method

method for locating peaks

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

Value

No value returned; the files are simply created.

Details

Reads in information from each file created by run.baselines, calls locate.peaks to find potential peaks, and writes the output to a file in directory peak.dir. The name of each new file is the same as the name of the old file with “.RData” replaced by “\_peaks.RData”. The resulting file contains the data frame all.peaks, which has columns

Center_hat

estimated mass of peak

Max_hat

estimated intensity of peak

Width_hat

estimated width of peak

and is ready to be used by run.lrg.peaks. The parameters gengamma.quantiles and peak.thresh are relevant only if calc.all.peaks = FALSE. In that case, if gengamma.quantiles == TRUE, then peak.thresh is interpreted as a multiplier for the baseline. Anything larger than peak.thresh times the estimated baseline is declared to be a real peak. If gengamma.quantiles == FALSE, then peak.thresh is interpreted as two-thirds of the value of $K$ used in a Tukey's biweight estimation of center and scale (so roughly equal to the number of standard deviations above the mean for iid normal data). Anything with weight zero in the calculation is then declared to be a real peak.

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.