Description Usage Arguments Details Value Note Author(s) References See Also
Extracts the parameters in the file specified by par.file
and returns them in list form.
1 | extract.pars(par.file = "parameters.RData", root.dir = ".")
|
par.file |
string containing name of parameters file |
root.dir |
string containing directory of parameters file to be extracted from |
Used by run.analysis
to record all the parameter choices in an analysis for future reference.
A list with the following components:
add.norm |
logical; whether to normalize additively or multiplicatively on the log scale |
add.par |
additive parameter for |
align.fcn |
function (and inverse) to apply to masses before (and after) applying |
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 |
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 |
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 |
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 |
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 |
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 |
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.
Don Barkauskas (barkda@wald.ucdavis.edu)
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.
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