run.strong.peaks {FTICRMS} | R Documentation |
Takes the file generated by run.peaks
, extracts all peaks that are “large” in
all samples, and writes the results to a file.
run.strong.peaks(cor.thresh = 0.8, isotope.dist = 7, pre.align = FALSE, root.dir = ".", lrg.dir, lrg.file = "lrg_peaks.RData", overwrite = FALSE, use.par.file = FALSE, par.file = "parameters.RData")
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 two-component list (of the form described in the Note section below) to be used before identifying peaks from different spectra |
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 |
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 |
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
.
No value returned; the file is simply created.
If use.par.file = TRUE
, then the parameters read in from the file overwrite any arguments entered in the
function call.
pre.align = FALSE
is used if the spectra have already been aligned by the mass spectroscopists.
Otherwise, it can either be a vector of additive shifts to be applied to the spectra, or a list with
components targets
and actual
. In the last case, targets
is a vector of target
masses, and actual
is a matrix with length(targets)
columns and a row for each spectrum,
actual[i,j]
being the mass in spectrum i
that should be matched exactly to
target[j]
, with NA
being a valid entry in actual
. The matching is done (depending
on the number of non-missing values in row i
) either with a simple shift (one non-missing value),
an affine transformation (two non-missing values), a piecewise affine transformation (three non-missing
values), or an interpolation spline (four or more non-missing values).
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.lrg.peaks
, run.cluster.matrix
,
interpSpline