run.analysis {FTICRMS}R Documentation

Test for Significant Peaks in FT-ICR MS by Controlling FDR

Description

Takes the file generated by run.cluster.matrix and tests the peaks using Benjamini-Hochberg to control the False Discovery Rate.

Usage

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", 
             ...)

Arguments

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

Details

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

Value

No value returned; the file is simply created.

Note

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.

Author(s)

Don Barkauskas (barkda@wald.ucdavis.edu)

References

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.

See Also

run.strong.peaks


[Package FTICRMS version 0.6 Index]