extract.pars: Extract Parameters from File

In FTICRMS: Programs for Analyzing Fourier Transform-Ion Cyclotron Resonance Mass Spectrometry Data

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 ([e-mail])

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

FTICRMS documentation built on May 1, 2019, 10:53 p.m.