make.par.file {FTICRMS} | R Documentation |
Creates a file of parameters that can be read by the functions in the FTICRMS
package
make.par.file(covariates, form, par.file = "parameters.RData", root.dir = ".", ...)
covariates |
data frame with rownames given by raw data files |
form |
object of class “formula ” to be used for testing using covariates |
par.file |
string containing name of file |
root.dir |
string containing location for file |
... |
parameters whose default values are to be overwritten (see below) |
Creates a file with name given by par.file
in directory given by root.dir
which
contains values for all of the parameters used in the programs in the FTICRMS
package.
The possible parameters that can be included in ...
, their default values, their
descriptions, and the program(s) in which they are used are as follows:
add.norm = TRUE | logical; whether to normalize additively or multiplicatively on the log scale | run.analysis |
add.par = 0 | additive parameter for "shiftedlog" or "glog" options for trans.method | run.cluster.matrix , run.lrg.peaks , run.peaks |
align.method = "spline" | alignment algorithm for peaks | run.cluster.matrix |
base.dir = paste(root.dir, "/Baselines", sep = "") | directory for baseline files | run.baselines , run.cluster.matrix , run.lrg.peaks , run.peaks |
calc.all.peaks = FALSE | logical; whether to calculate all possible peaks or only sufficiently large ones | run.cluster.matrix , run.lrg.peaks , run.peaks |
cluster.constant = 10 | NA | run.cluster.matrix |
cluster.method = "ppm" | NA | run.cluster.matrix |
cor.thresh = 0.8 | threshold correlation for declaring isotopes | run.strong.peaks |
FDR = 0.1 | False Discovery Rate in Benjamini-Hochberg test | run.analysis |
gengamma.quantiles = TRUE | logical; whether to use generalized gamma quantiles when calculating large peaks | run.lrg.peaks , run.peaks |
halve.search = FALSE | logical; whether to use a halving-line search if step leads to smaller value of function | run.baselines |
isotope.dist = 7 | maximum distance for declaring isotopes | run.analysis , run.cluster.matrix , run.strong.peaks |
lrg.dir = paste(root.dir, "/Large_Peaks", sep = "") | directory for large peaks file | run.analysis , run.cluster.matrix , run.lrg.peaks , run.strong.peaks |
lrg.file = "lrg_peaks.RData" | name of file for storing large peaks | run.analysis , run.cluster.matrix , run.lrg.peaks , run.strong.peaks |
lrg.only = TRUE | logical; whether to consider only peaks that have at least one “large”peak; i.e., identified by run.lrg.peaks | run.analysis , run.cluster.matrix |
masses = NULL | specific masses to test | run.analysis , run.cluster.matrix |
max.iter = 40 | convergence criterion in baseline calculation | run.baselines |
neg.div = NA | negativity divisor in baseline calculation | run.baselines |
neg.norm.by = c("baseline", "overestimate", "constant") | method for negativity penalty in baseline analysis | run.baselines |
normalization = "common" | type of normalization to use on spectra before statistical anlaysis | run.analysis |
num.pts = 5 | number of consecutive points needed for peak fitting | run.cluster.matrix , run.peaks |
oneside.min = 1 | minimum number of points on each side of local maximum for peak fitting | run.cluster.matrix , run.peaks |
overwrite = FALSE | whether to replace exisiting files with new ones | All six programs |
par.file = "parameters.RData" | string containing name of parameters file | All six programs |
peak.dir = paste(root.dir, "/All_Peaks", sep = "") | directory for peak location files | run.cluster.matrix , run.lrg.peaks , run.peaks |
peak.method = "parabola" | method for locating peaks | run.cluster.matrix , run.peaks |
peak.thresh = 3.798194 | threshold for declaring large peak | run.lrg.peaks , run.peaks |
pre.align = FALSE | shifts to apply before running run.strong.peaks | run.cluster.matrix , run.strong.peaks |
pval.fcn = "default" | function to calculate p-values if use.t.test = FALSE | run.analysis |
R2.thresh = 0.98 | R^2 value needed for peak fitting | run.cluster.matrix , run.peaks |
raw.dir = paste(root.dir, "/Raw_Data", sep = "") | directory for raw data files | run.baselines |
rel.conv.crit = TRUE | whether convergence criterion should be relatiev to current baseline estimate | run.baselines |
repl.method = max | how to deal with replicates | run.analysis |
res.dir = paste(root.dir, "/Results", sep = "") | directory for result file | run.analysis |
res.file = "analyzed.RData" | name for results file | run.analysis |
root.dir = "." | directory for parameters file and raw data | All six programs |
sm.div = NA | smoothness divisor in baseline calculation | run.baselines |
sm.norm.by = c("baseline", "overestimate", "constant") | method for smoothness penalty in baseline analysis | run.baselines |
sm.ord = 2 | order of derivative to kill in baseline analysis | run.baselines |
sm.par = 1e-11 | smoothing parameter for baseline calculation | run.baselines |
subtract.base = FALSE | logical; whether to subtract calculated baseline from spectrum | run.cluster.matrix , run.lrg.peaks , run.peaks |
tol = 5e-8 | convergence criterion in baseline calculation | run.baselines |
trans.method = "shiftedlog" | data transformation method | run.cluster.matrix , run.lrg.peaks , run.peaks |
use.t.test = FALSE | whether to use a t-test to calculate p-values | run.analysis |
zero.rm = TRUE | whether to replace zeros in spectra with average of surrounding values | run.baselines |
No value returned; the file par.file
is simply created in root.dir
.
do.call(make.par.file, extract.pars())
recreates the original parameter file.
See the individual function help pages for each function for more detailed descriptions of the above parameters.
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.
Xi, Y. and Rocke, D.M. (2008) “Baseline Correction for NMR Spectroscopic Metabolomics Data Analysis”. BMC Bioiniformatics, 9:324.