baseline {FTICRMS}R Documentation

Calculate Baselines for FT-ICR MS Spectra

Description

Computes an estimated baseline curve for a spectrum by a method of Rocke and Xi.

Usage

baseline(spect, sm.par = 1.1E-9, neg.pen = sqrt(pi/2), 
         k.biweight = 6, max.iter = 30, 
         frac.changed = 0.001, xvals = 1:length(spect))

Arguments

spect vector containing the intensities of the spectrum
sm.par smoothing parameter
neg.pen negativity penalty
k.biweight parameter for robust center and scale estimation using Tukey's biweight
max.iter maximum number of iterations before giving up
frac.changed maximal fraction of indicators that can switch for convergence
xvals vector containing the mass locations of the spectrum; currently not used in the algorithm

Details

The algorithm works by maximizing the objective function

F({b[i]}) = sum_{i=1}^{n}b[i] - A[1] sum_{i=2}^{n-1}(b[i-1]-2b[i]+b[i+1])^2 - A[2] sum_{i=1}^n[max{b[i]-y[i],0}]^2

The value of xvals is currently irrelevant; future implementations may use it.

Experimentally, the smoothing factor sm.par should be as large as possible without giving obviously incorrect results; it corresponds to A^{*} in Equation (10) in Xi and Rocke.

The default value of neg.pen is calculated under the assumption of iid N(0,1) noise and corresponds to B^{*} in Equation (10) in Xi and Rocke.

The default value of frac.changed is the smallest one for which the algorithm reliably terminates before max.iter iterations are reached for any reasonable value of max.iter.

Value

A list containing the following items:

baseline The computed baseline
noise The estimated noise standard deviation
iter The number of iterations for convergence
changed Numeric vector of length iter containing the number of indicator variables that switched value on each iteration

Note

The algorithm used was developed by Yuanxin Xi and David Rocke. This program is based on a Matlab program by Yuanxin Xi.

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

Xi, Y. and Rocke, D.M. (2008) “Baseline Correction for NMR Spectroscopic Metabolomics Data Analysis”. BMC Bioinformatics, 9:324.

See Also

run.baselines


[Package FTICRMS version 0.6 Index]