Description Usage Arguments Details Value Note Author(s) References See Also
Takes the file generated by run.peaks
, extracts all peaks that are
“large” in all samples, and writes the results to a file.
1 2 3 4 5 |
cor.thresh |
threshold correlation for declaring isotopes |
isotope.dist |
maximum distance for declaring isotopes |
pre.align |
either |
align.method |
alignment algorithm for peaks |
align.fcn |
function (and inverse) to apply to masses before (and after) applying |
root.dir |
directory for parameters file and raw data |
lrg.dir |
directory for large peaks file; default is |
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 |
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
and other parameters are entered into the function
call, then the parameters entered in the function call overwrite those read in
from the file. Note that this is opposite from the behavior for
FTICRMS versions 0.7 and earlier.
If align.fcn
is not NA
, then it should consist of a list with components
fcn
and inv
, each of class function
. align.fcn$fcn
should
take a vector of masses as its argument and return a vector of transformed masses.
(Typically, this will be transforming to the frequency domain; see Zhang (2005).)
align.fcn$inv
should be the inverse function of align.fcn$fcn
.
If align.method == "leastsq"
, it is strongly recommended that you supply a
value for align.fcn
that makes the masses (approximately) equally-spaced.
align.method
can be abbreviated. If align.method == "spline"
,
then alignment consists of making the transformed masses of the strong peaks all
agree exactly with their means, then shifting the rest of the transformed masses
via a cubic interpolation spline generated using
interpSpline
. If align.method == "PL"
, then the
same is done but interpolation is piecewise linear between the strong peaks. If
align.method == "leastsq"
, then the transformed masses of the strong peaks
are aligned to their means using a least-squares affine fit for each spectrum.
In any of these cases, if there are no strong peaks, align.method
is
changed to "none"
with a warning. If there is exactly one strong peak,
then alignment is by a simple shift in each spectrum on the transformed masses.
If there are exactly two strong peaks, then the alignment is by a simple affine
transformation on the transformed masses in each spectrum. If
align.method == "spline"
and there are exactly three strong peaks, then
alignment is piecewise affine on the transformed masses (i.e., identical to
using align.method = "PL"
).
pre.align = FALSE
is used if the spectra have 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
, actual
, and align.method
. 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
alignment is then done row-by-row as in the description in the above paragraph,
depending on the number of non-missing values in row i
).
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.
Zhang, L.-K. et al. (2005) “Accurate mass measurements by Fourier transform mass spectrometry”. Mass Spectrom Rev, 24:2, 286–309.
run.lrg.peaks
, run.cluster.matrix
,
interpSpline
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