run.peaks {FTICRMS} | R Documentation |
Takes baseline-corrected data and locates potential peaks in the spectra.
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)
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 |
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.
No value returned; the files are simply created.
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.
Don Barkauskas (barkda@wald.ucdavis.edu)
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.
run.baselines
, run.lrg.peaks
, locate.peaks