run.lrg.peaks {FTICRMS} | R Documentation |
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.
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)
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 |
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 |
run.strong.peaks
.
No value returned; the file is simply created.
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).
Don Barkauskas (barkda@wald.ucdavis.edu)
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.