extract.pars {FTICRMS}R Documentation

Extract Parameters from File

Description

Extracts the parameters in the file specified by par.file and returns them in list form.

Usage

extract.pars(par.file = "parameters.RData", root.dir = ".")

Arguments

par.file string containing name of parameters file
root.dir string containing directory of parameters file to be extracted from

Details

Used by run.analysis to record all the parameter choices in an analysis for future reference.

Value

A list with the following components:

add.norm logical; whether to normalize additively or multiplicatively on the log scale
add.par additive parameter for "shiftedlog" or "glog" options for trans.method
align.fcn function (and inverse) to apply to masses before (and after) applying align.method
align.method alignment algorithm for peaks
base.dir directory for baseline files
bhbysubj logical; whether to look for number of large peaks by subject (i.e., combining replicates) or by spectrum
calc.all.peaks whether to calculate all possible peaks or only sufficiently large ones
cluster.constant parameter used in running cluster.method
cluster.method method for determining when two peaks from different spectra are the same
cor.thresh threshhold correlation for declaring isotopes
covariates data frame containing covariates used in analysis
FDR False Discovery Rate in Benjamini-Hochberg test
FTICRMS.version Version of FTICRMS that created file
form formula used in use.model
gengamma.quantiles whether to use generalized gamma quantiles when calculating large peaks
halve.search whether to use a halving-line search if step leads to smaller value of function
isotope.dist maximum distance for declaring isotopes
lrg.dir directory for significant peaks file
lrg.file name of file for storing large peaks
lrg.only whether to consider only peaks that have at least one “large” peak; i.e., identified by run.lrg.peaks
masses specific masses to test
max.iter convergence criterion in baseline calculation
min.spect minimum number of spectra necessary for peak to be used in run.analysis
neg.div negativity divisor in baseline calculation
neg.norm.by method for negativity penalty in baseline analysis
norm.peaks which peaks to use in normalization
norm.post.repl logical; whether to normalize after combining replicates
normalization type of normalization to use on spectra before statistical analysis
num.pts number of points needed for peak fitting
oneside.min minimum number of points on each side of local maximum for peak fitting
overwrite whether to replace existing files with new ones
par.file string containing name of parameters file
peak.dir directory for peak location files
peak.method method for locating peaks
peak.thresh threshold for declaring large peak
pre.align shifts to apply before running run.strong.peaks
pval.fcn function to calculate p-values
R2.thresh R^2 value needed for peak fitting
raw.dir directory for raw data files
rel.conv.crit whether convergence criterion should be relative to size of current baseline estimate
repl.method how to deal with replicates
res.dir directory for result file
res.file name for results file
root.dir directory for parameters file and raw data directory
sm.div smoothness divisor in baseline calculation
sm.norm.by method for smoothness penalty in baseline analysis
sm.ord order of derivative to penalize in baseline analysis
sm.par smoothing parameter for baseline calculation
subs subset of spectra to use for analysis
subtract.base whether to subtract calculated baseline from spectrum
tol convergence criterion in baseline calculation
trans.method data transformation method
use.model what model to apply to data
zero.rm whether to replace zeros in spectra with average of surrounding values

Note

do.call(make.par.file, extract.pars()) recreates the original parameter file

align.method, cluster.method, neg.norm.by, normalization, peak.method, sm.norm.by, and trans.method can be abbreviated.

See make.par.file for a summary of which programs use each of the parameters in the list.

Author(s)

Don Barkauskas (barkda@wald.ucdavis.edu)

References

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.

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.

See Also

make.par.file, run.analysis


[Package FTICRMS version 0.8 Index]