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 |
threshhold correlation for declaring two peaks to be isotopes |
isotope.dist |
maximum difference in mass for declaring two peaks to be 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 |
string containing location of raw data directory |
lrg.dir |
directory for significant peaks file; default is paste(root.dir, "/Large_Peaks", sep = "") |
lrg.file |
string containing name of significant peaks file |
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 |
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.
A peak is “large” if its value of Max_hat
is at least numsds
standard
deviations above the mean value in the data generated by run.peaks
for the
spectrum it comes from.
If use.par.file = TRUE
, then the parameters read in from the file overwrite any arguments entered in the
function call.
pre.align
is used if the spectra have not already been aligned by the mass spectroscopists.
If it is not FALSE
, 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. et al. (2008) “Detecting glycan cancer biomarkers in serum samples using MALDI FT-ICR mass spectrometry data”. Submitted to Bioinformatics
run.lrg.peaks
, run.cluster.matrix
,
interpSpline