An Efficient Algorithm for Clustering of Large-Scale Mass Spectrometry Data

TL;DR

The paper introduces CAMS, an efficient clustering algorithm for large-scale mass spectrometry data that improves spectral clustering accuracy and reduces computational time using a novel F-set metric and graph theoretic framework.

Contribution

CAMS is a new clustering algorithm that enhances sensitivity and confidence in spectral assignment for large mass spectrometry datasets, using a novel F-set metric and graph theory.

Findings

High clustering accuracy on real datasets

Significant reduction in computational time

Improved spectral interpretation for low S/N spectra

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.