make.par.file: Create Parameter File for FT-ICR MS Analysis

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

Description

Creates a file of parameters that can be read by the functions in the FTICRMS package

Usage

make.par.file(covariates, form, par.file = "parameters.RData", root.dir = ".", ...)

Arguments

covariates	data frame with rownames given by raw data files with extensions (e.g., “.txt”) stripped
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)

Details

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.fcn = NA	function (and inverse) to apply to masses before (and after) applying align.method	run.cluster.matrix, run.strong.peaks
align.method = "spline"	alignment algorithm for peaks	run.cluster.matrix, run.strong.peaks
base.dir = paste(root.dir, "/Baselines", sep="")	directory for baseline files	run.baselines, run.cluster.matrix, run.lrg.peaks, run.peaks
bhbysubj = FALSE	logical; whether to look for number of large peaks by subject (i.e., combining replicates) or by spectrum	run.cluster.matrix, run.analysis
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	parameter used in running cluster.method	run.cluster.matrix
cluster.method = "ppm"	method for determining when two peaks from different spectra are the same	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
FTICRMS.version = "0.8"	Version of FTICRMS that created file	Archiving purposes only
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 = NA	specific masses to test	run.analysis, run.cluster.matrix
max.iter = 20	convergence criterion in baseline calculation	run.baselines
min.spect = 1	minimum number of spectra necessary for peak to be used in run.analysis	run.cluster.matrix
neg.div = NA	negativity divisor in baseline calculation	run.baselines
neg.norm.by = "baseline"	method for negativity penalty in baseline analysis	run.baselines
norm.peaks = "common"	which peaks to use in normalization	run.analysis
norm.post.repl = FALSE	logical; whether to normalize after combining replicates	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	logical; whether to replace existing 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; default is overall p-value of test	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 relative to size of current baseline estimate	run.baselines
repl.method = "max"	how to deal with replicates	run.analysis
res.dir = paste(root.dir, "/Results", sep="")	directory for results 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 = "baseline"	method for smoothness penalty in baseline analysis	run.baselines
sm.ord = 2	order of derivative to penalize in baseline analysis	run.baselines
sm.par = 1e-11	smoothing parameter for baseline calculation	run.baselines
subs	subset of spectra to use for analysis	run.lrg.peaks, run.analysis
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.model = "lm"	what model to apply to data	run.analysis
zero.rm = TRUE	whether to replace zeros in spectra with average of surrounding values	run.baselines

Value

No value returned; the file par.file is simply created in root.dir.

Note

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.

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

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.

Xi, Y. and Rocke, D.M. (2008) “Baseline Correction for NMR Spectroscopic Metabolomics Data Analysis”. BMC Bioiniformatics, 9:324.

See Also

extract.pars

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