Analysis of mass spectrometry data using sub-spectra.
BACKGROUND
Spectra resulting from Surface-Enhanced Laser Desorption/Ionisation (SELDI) mass spectrometry measurements are constructed by combining sub-spectra, each of which are the result of a single firing of the laser responsible for the process of desorption/ionisation. These firings are performed at different locations of the spot on which the sample is analysed. The final spectrum is then constructed by summing over all these sub-spectra. This process is sub-optimal in that it can average out peaks from peptides that are present in low abundance or are unevenly distributed across the spot, particularly because the amount of noise varies considerably between sub-spectra. This argues for analysing sub-spectra separately and combining results afterwards.


RESULTS
Here, we propose to analyse these sub-spectra one-by-one and combine the results using a framework which includes a significance test. This allows one to, for the first time, attach a confidence measure to detected peaks, based on the signal strength of a peak across sub-spectra. In a comparison with three other approaches the sub-spectral approach achieves a higher sensitivity and a low FDR. We further introduce the notion of peak-bags, which provide rich information about the sub-spectral contributions to a given peak.


CONCLUSIONS
The proposed procedure offers better control over the process of distinguishing signal from noise, resulting in an improved performance over other available methods. Moreover, our method provides an implicit deconvolution of peaks, yielding insight in the actual shape of a peak, potentially aiding in a deeper understanding of peak distribution.


BACKGROUND
Implementations of the algorithm in R are available upon request.
Meuleman
Wouter
W
Bioinformatics and Statistics, Department of Molecular Biology, The Netherlands Cancer Institute, Amsterdam, The Netherlands. w.meuleman@tudelft.nl
Engwegen
Judith Y M N
JY
Gast
Marie-Christine W
MC
Wessels
Lodewyk F A
LF
Reinders
Marcel J T
MJ
D000465:Algorithms; D010363:Pattern Recognition, Automated; D010455:Peptides / Q000032:analysis / Q000737:chemistry*; D012815:Signal Processing, Computer-Assisted; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
D010455:Peptides