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

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)

Value

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

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`

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.