An efficient algorithm for clustering of large-scale mass spectrometry data Conference

Saeed, F, Pisitkun, T, Knepper, MA et al. (2012). An efficient algorithm for clustering of large-scale mass spectrometry data . 516-519. 10.1109/BIBM.2012.6392738

cited authors

  • Saeed, F; Pisitkun, T; Knepper, MA; Hoffert, JD

authors

abstract

  • High-throughput spectrometers are capable of producing data sets containing thousands of spectra for a single biological sample. These data sets contain a substantial amount of redundancy from peptides that may get selected multiple times in a LC-MS/MS experiment. In this paper, we present an efficient algorithm, CAMS (Clustering Algorithm for Mass Spectra) for clustering mass spectrometry data which increases both the sensitivity and confidence of spectral assignment. CAMS utilizes a novel metric, called F-set, that allows accurate identification of the spectra that are similar. A graph theoretic framework is defined that allows the use of F-set metric efficiently for accurate cluster identifications. The accuracy of the algorithm is tested on real HCD and CID data sets with varying amounts of peptides. Our experiments show that the proposed algorithm is able to cluster spectra with very high accuracy in a reasonable amount of time for large spectral data sets. Thus, the algorithm is able to decrease the computational time by compressing the data sets while increasing the throughput of the data by interpreting low S/N spectra. © 2012 IEEE.

publication date

  • December 1, 2012

Digital Object Identifier (DOI)

International Standard Book Number (ISBN) 13

start page

  • 516

end page

  • 519