run.cluster.matrix: Identify Equivalent Peaks from Different Subjects

Description

Takes the file generated by run.lrg.peaks, identifies equivalent peaks in each spectrum, and fills in missing values.

Usage

run.cluster.matrix(pre.align = FALSE, align.method = c("PL", "spline", "affine", "none"), align.fcn = NA, trans.method = c("shiftedlog", "glog", "none"), add.par = 0, subtract.base = FALSE,  lrg.only = TRUE, calc.all.peaks = FALSE,  masses = NA, isotope.dist = 7,  cluster.method = c("ppm", "constant", "usewidth"),  cluster.constant = 10, num.pts = 5,  R2.thresh = 0.98, oneside.min = 1, min.spect = 1, peak.method = c("parabola", "locmaxes"),  bhbysubj = TRUE, covariates, root.dir = ".", base.dir, peak.dir, lrg.dir, lrg.file = "lrg_peaks.RData", overwrite = FALSE, use.par.file = FALSE, par.file = "parameters.RData")

Arguments

pre.align

either FALSE, or a numeric vector of shifts to apply to spectra, or a four-component list (of the form described in the Note section below) to be used before identifying peaks from different spectra

align.method

alignment algorithm for peaks

align.fcn

function (and inverse) to apply to masses before (and after) applying align.method; see below

trans.method

type of transformation to use on spectra before statistical analysis

add.par

additive parameter for "shiftedlog" or "glog" options for trans.method

subtract.base

logical; whether to subtract calculated baseline from spectrum

lrg.only

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

calc.all.peaks

logical; whether to calculate all possible peaks or only sufficiently large ones

masses

specific masses to test

isotope.dist

maximum distance for declaring isotopes

cluster.method

method for determining when two peaks from different spectra are the same

cluster.constant

parameter used in running cluster.method

num.pts

number of consecutive points needed for peak fitting

R2.thresh

$R^2$ value needed for peak fitting

oneside.min

minimum number of points on each side of local maximum for peak fitting

min.spect

minimum number of spectra necessary for peak to be used in run.analysis

peak.method

method for locating peaks

bhbysubj

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

covariates

data frame with rownames given by raw data files with extensions (e.g., “.txt”) stripped; only needed if bhbysubj == TRUE

root.dir

directory for parameters file and raw data

base.dir

directory for baseline files; default is paste(root.dir, "/Baselines", sep = "")

peak.dir

directory for peak location files; default is paste(root.dir, "/All_Peaks", sep = "")

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

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

Value

No value returned; the file is simply created.

Details

Reads in information from file created by run.strong.peaks, calculates the cluster matrix, fills in missing values, and overwrites the file named lrg.file in lrg.dir. The resulting file contains variables

amps

data frame of amplitudes created by run.strong.peaks

centers

data frame of centers created by run.strong.peaks

clust.mat

data frame with columns given by samples and rows given by the distinct peaks in the samples

lrg.mat

data frame of same size as clust.mat with entries given by TRUE if the peak was large in that spectrum and FALSE otherwise

lrg.peaks

the data frame of significant peaks created by run.lrg.peaks

num.lrg

number of subjects (or spectra if bhbysubj == TRUE) with a large peak at the corresponding mass

and is ready to be used by run.analysis.

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.

Zhang, L.-K. et al. (2005) “Accurate mass measurements by Fourier transform mass spectrometry”. Mass Spectrom Rev, 24:2, 286--309.