run.analysis: 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, norm.post.repl = FALSE,  norm.peaks = c("common", "all", "none"), normalization,  add.norm = TRUE,  repl.method = "max", use.model = "lm", pval.fcn = "default", lrg.only = TRUE, masses = NA, 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", bhbysubj = TRUE, subs, ...)

Arguments

form

object of class “formula” to be used by use.model for testing using covariates

covariates

data frame containing covariates used in analysis

FDR

False Discovery Rate in Benjamini-Hochberg test

norm.post.repl

logical; whether to normalize after combining replicates

norm.peaks

which peaks to use in normalization

normalization

type of normalization to use on spectra before statistical analysis; kept for compatibility (see below)

add.norm

logical; whether to normalize additively or multiplicatively on the log scale

repl.method

function or string representing the name of a function; how to deal with replicates

use.model

function or string representing the name of a function; what test to apply to data

pval.fcn

function to extract p-values; default is overall p-value of test

lrg.only

logical; whether to consider only peaks that have at least one “large” peak; i.e., identified by run.lrg.peaks

masses

specific masses to test

isotope.dist

maximum distance for declaring isotopes

root.dir

directory for parameters file and raw data

lrg.dir

directory for large peaks file; default is paste(root.dir, "/Large_Peaks", sep = "")

lrg.file

name of file to store large peaks in

res.dir

directory for results file; default is paste(root.dir, "/Results", sep = "")

res.file

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

bhbysubj

logical; whether to look for number of large peaks by subject (i.e., combining replicates) or by spectrum

subs

subset of spectra to use for analysis; see below

...

additional parameters to be passed to use.model

Value

No value returned; the file is simply created.

Details

Reads in information from file created by run.cluster.matrix and creates a file named res.file in directory res.dir which contains the following variables:

amps

matrix of transformed amplitudes of alignment peaks

bysubjvar

a vector which tells which rows of covariates are identified as the same subject

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

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.