locate.peaks {FTICRMS} | R Documentation |
Locates peaks in FT-ICR MS spectra assuming that the peaks are roughly parabolic on the log scale.
locate.peaks(peak.base, num.pts = 5, R2.thresh = 0.98, oneside.min = 1, peak.method = "parabola", thresh = -Inf)
peak.base |
numeric matrix with two columns containing the masses and the pre-transformed spectrum intensities |
num.pts |
minimum number of points needed to have a peak |
R2.thresh |
minimum R^2 needed to have a peak |
oneside.min |
minimum number of points needed on each side of the local maximum |
peak.method |
how to locate peaks; currently the only options are "parabola" and "locmax" |
thresh |
only local maxes that are larger than this will be checked to see if they are peaks |
If peak.method = "parabola"
, the algorithm works by locating local maxima in the spectrum,
then seeing if any num.pts
consecutive points with at least oneside.min
point(s) on each side
of the local maximum have a coefficient of determination (R^2) of at least R2.thresh
when fitted with a
quadratic. If, in addition, the coefficient of the squared term is negative, then this is declared a peak
and the vertex of the corresponding parabola is located. The coordinates of the vertex give the components
Center_hat
and Max_hat
in the return value. The Width_hat
component is the negative
reciprocal of the coefficient of the squared term.
If peak.method = "locmax"
, then the algorithm merely returns the set of local maxima larger than
thresh
, and the Width_hat
component of the return value is NA
.
A data frame with columns
Center_hat |
estimated mass of peak |
Max_hat |
estimated intensity of peak |
Width_hat |
estimated width of peak |
An extremely large value for Width_hat
most likely indicates a bad fit.
Using peak.method = "locmax"
will vastly speed up the runtime, but may affect the
quality of the analysis.
As noted in both papers by Barkauskas, a typical FT-ICR MS spectrum has far more peaks than can be
accounted for by actual compounds. Thus, defining a good value of thresh
will vastly speed up the
computation without materially affecting the analysis.
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.