| run.analysis {FTICRMS} | R Documentation |
Takes the file generated by run.cluster.matrix and tests the peaks using Benjamini-Hochberg
to control the False Discovery Rate.
run.analysis(form, covariates, FDR = 0.1, normalization = "common",
add.norm = TRUE, repl.method = max, use.t.test = TRUE,
pval.fcn = "default", lrg.only = TRUE, masses = NULL,
isotope.dist = 7, root.dir = ".", lrg.dir,
lrg.file = "lrg.peaks.RData", res.dir,
res.file = "analyzed.RData", overwrite = FALSE,
use.par.file = FALSE, par.file = "parameters.RData",
...)
form |
formula used in t.test or lm |
covariates |
data frame containing covariates used in analysis |
FDR |
False Discovery Rate to use in Benjamini-Hochberg test |
normalization |
type of normalization to use on spectra before statistical analysis; currently, only "common",
"postbase", "postrepl", and "none" are supported |
add.norm |
logical; whether to normalize additively or multiplicatively on the log scale |
repl.method |
function or string representing a function; how to deal with replicates |
use.t.test |
logical; whether to use t.test to get p-values |
pval.fcn |
default value gives p-value of overall F-statistic of test; see below for form to be used for user-defined values |
lrg.only |
whether to consider only peaks that have at least one peak “significant”; i.e.,
identified by run.lrg.peaks |
masses |
numeric vector of specific masses to test |
isotope.dist |
maximum number of isotope peaks to look at (in addition to main peak) |
root.dir |
string containing location of raw data directory |
lrg.dir |
directory for significant peaks file; default is paste(root.dir, "/Large_Peaks", sep = "") |
lrg.file |
string containing name for significant peaks file |
res.dir |
directory for results file; default is paste(root.dir, "/Results", sep = "") |
res.file |
string containing name for results file |
overwrite |
logical; whether to replace existing files with new ones |
use.par.file |
logical; if TRUE, then parameters are read from
par.file in directory root.dir |
par.file |
string containing name of parameters file |
... |
additional parameters to be passed to t.test or pval.fcn |
Reads in information from file created by run.strong.peaks and creates a file named
res.file in res.dir which contains variables
amps | matrix of transformed amplitudes of alignment peaks |
centers | matrix of calculated masses of alignment peaks |
clust.mat | matrix of transformed amplitudes of peaks used in statistical testing |
min.FDR | FDR level required to get at least one significant test given the starting set of peaks |
sigs | matrix containing all tests which are significant under at least one scenario |
which.sig | matrix containing all peaks tested |
parameter.list | if use.par.file = TRUE, a list generated by extract.pars; otherwise not defined |
No value returned; the file is simply created.
If use.par.file = TRUE, then the parameters read in from the file overwrite any arguments entered in the
function call.
To analyze replicates as independent samples, use repl.method = "none". This will also speed up the
run time if there are no replicates in the data set.
The normalization schemes are as follows: "common" divides all peak heights in each spectrum
by the average peak height of the peaks in that spectrum in amps; "postbase" divides
all peaks heights in each spectrum by the average of of all peak heights in that spectrum; and
"postrepl" first combines replicates by applying repl.method to the peaks and
then does "postbase".
If masses is not NULL, then the listed masses plus anything that could be in the first six isotope peaks
of each mass are tested.
If something other than the p-value for the overall F-statistic is needed, then the user-defined function for pval.fcn should have the form function(form, dat, ...), where form and dat are as in lm; and should have a return value of the desired p-value.
Don Barkauskas (barkda@wald.ucdavis.edu)
Barkauskas, D.A. et al. (2008) “Detecting glycan cancer biomarkers in serum samples using MALDI FT-ICR mass spectrometry data”. Submitted to Bioinformatics
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.