A comprehensive approach to the analysis of matrix-assisted laser desorption/ionization-time of flight proteomics spectra from serum samples

Keith A. Baggerly, Jeffrey S. Morris, Jing Wang, David Gold, Lian Chun Xiao, Kevin R. Coombes

Research output: Contribution to journalArticlepeer-review

134 Scopus citations

Abstract

For our analysis of the data from the First Annual Proteomics Data Mining Conference, we attempted to discriminate between 24 disease spectra (group A) and 17 normal spectra (group B). First, we processed the raw spectra by (i) correcting for additive sinusoidal noise (periodic on the time scale) affecting most spectra, (ii) correcting for the overall baseline level, (iii) normalizing, (iv) recombining fractions, and (v) using variable-width windows for data reduction. Also, we identified a set of polymeric peaks (at multiples of 180.6 Da) that is present in several normal spectra (B1-B8). After data processing, we found the intensities at the following mass to charge (m/z) values to be useful discriminators: 3077, 12 886 and 74 263. Using these values, we were able to achieve an overall classification accuracy of 38/41 (92.6%). Perfect classification could be achieved by adding two additional peaks, at 2476 and 6955. We identified these values by applying a genetic algorithm to a filtered list of m/z values using Mahalanobis distance between the group means as a fitness function.

Original languageEnglish (US)
Pages (from-to)1667-1672
Number of pages6
JournalProteomics
Volume3
Issue number9
DOIs
StatePublished - Sep 1 2003
Externally publishedYes

Keywords

  • Cross validation
  • Data cleaning
  • Discrimination
  • Genetic algorithm
  • Mahalanobis distance

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology

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