run.cluster.matrix {FTICRMS} | R Documentation |
Takes the file generated by run.lrg.peaks
, identifies equivalent peaks in each spectrum,
and fills in missing values.
run.cluster.matrix(pre.align = FALSE, align.method = "spline", trans.method = "shiftedlog", add.par = 10, lrg.only = TRUE, masses = NULL, isotope.dist = 7, root.dir = ".", base.dir, peak.dir, lrg.dir, lrg.file = "lrg.peaks.RData", overwrite = FALSE, use.par.file = FALSE, par.file = "parameters.RData")
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 |
align.method |
type of alignment to use on spectra before statistical analysis; currently, only "spline"
and "none" are supported |
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 |
lrg.only |
whether to consider only peaks that have at least one peak “significant”; i.e.,
identified by run.lrg.peaks |
masses |
numeric vector of specific masses to test |
isotope.dist |
maximum number of isotope peaks to look at (in addition to main peak) |
root.dir |
string containing location of raw data directory |
base.dir |
directory for baseline-corrected files; default is paste(root.dir, "/Baseline_Corrected", sep = "") |
peak.dir |
directory for peak location files; default is paste(root.dir, "/All_Peaks", sep = "") |
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.strong.peaks
, calculates the cluster matrix,
fills in missing values, and overwrites the file named lrg.file
in lrg.dir
.
The resulting file contains variables
amps | data frame of amplitudes created by run.strong.peaks |
centers | data frame of centers created by run.strong.peaks |
clust.mat | data frame with columns given by samples and rows given by the distinct peaks in the samples |
num.sig | vector of the number of peaks in each row of clust.mat which were not missing |
lrg.peaks | the data frame of significant peaks created by run.lrg.peaks |
and is ready to be used by run.strong.peaks
.
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
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.strong.peaks
, interpSpline