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 = FALSE, 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 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 |
whether to use a t-test to calculate p-values |
pval.fcn |
function to calculate p-values if use.t.test = FALSE ; default is overall p-value of F-test using lm |
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 |
whether to replace exisiting 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 alignment peaks from 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. (2009) “Statistical Analysis of Matrix-Assisted Laser Desorption/Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Data with Applications to Cancer Biomarker Detection”. Ph.D. dissertation, University of California at Davis.
Barkauskas, D.A. et al. (2009) “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.