run.all {FTICRMS} | R Documentation |
A wrapper that calls all six functions needed for a full analysis.
run.all(par.file = "parameters.RData", root.dir = ".")
par.file |
string containing the name of the parameters file |
root.dir |
string containing location of raw data directory and parameters file |
Requires par.file
to be in place before starting—for example by creating it with
make.par.file
.
Calls (in order) run.baselines
, run.peaks
, run.lrg.peaks
,
run.strong.peaks
, run.cluster.matrix
, and run.analysis
.
The analysis described in Barkauskas et al. (2008) can be reproduced using the following parameter values instead of the defaults:
add.par = 10 |
|
calc.all.peaks = TRUE |
|
gengamma.quantiles = FALSE |
|
neg.norm.by = "constant" |
|
peak.thresh = 4 |
|
sm.norm.by = "constant" |
|
subtract.base = TRUE |
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.
Benjamini, Y. and Hochberg, Y. (1995) “Controlling the false discovery rate: a practical and powerful approach to multiple testing.” J. Roy. Statist. Soc. Ser. B, 57:1, 289–300.
Xi, Y. and Rocke, D.M. (2008) “Baseline Correction for NMR Spectroscopic Metabolomics Data Analysis”. BMC Bioinformatics, 9:324.
make.par.file
, run.baselines
, run.peaks
,
run.lrg.peaks
, run.strong.peaks
, run.cluster.matrix
,
run.analysis