run.peaks {FTICRMS}R Documentation

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 = "shiftedlog", 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 = "parabola", calc.all.peaks = FALSE,
          gengamma.quantiles = TRUE, peak.thresh = 3.798194)

Arguments

trans.method type of transformation to use on spectra before statistical analysis; currently, only "shiftedlog", "glog", and "none" are supported
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 whether to replace exisiting 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

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 = TRUE, 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.

Value

No value returned; the files are simply created.

Note

If use.par.file = TRUE, then the parameters read in from the file overwrite any arguments entered in the function call.

Using calc.all.peaks = FALSE will speed up computation time immensely, but will affect the final result. It probably won't affect it much, but caveat emptor.

Author(s)

Don Barkauskas (barkda@wald.ucdavis.edu)

References

Barkauskas, D.A. (2009) “Statistical Analysis of Matrix-Assisted Laser Desorption/Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Data with Applications to Cancer Biomarker Detection”. Ph.D. dissertation, University of California at Davis.

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

See Also

run.baselines, run.lrg.peaks, locate.peaks


[Package FTICRMS version 0.7 Index]