extract.pars: 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

Value

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

Details

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

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.