Efficient model-based clustering for LC-MS data

Marta Łuksza, Bogusław Kluge, Jerzy Ostrowski, Jakub Karczmarski, Anna Gambin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Proteomic mass spectrometry is gaining an increasing role in diagnostics and in studies on protein complexes and biological systems. The issue of high-throughput data processing is therefore becoming more and more significant. The problems of data imperfectness, presence of noise and of various errors introduced during experiments arise. In this paper we focus on the peak alignment problem. As an alternative to heuristic based approaches to aligning peaks from different mass spectra we propose a mathematically sound method which exploits the model-based approach. In this framework experiment errors are modeled as deviations from real values and mass spectra are regarded as finite Gaussian mixtures. The advantage of such an approach is that it provides convenient techniques for adjusting parameters and selecting solutions of best quality. The method can be parameterized by assuming various constraints. In this paper we investigate different classes of models and select the most suitable one. We analyze the results in terms of statistically significant biomarkers that can be identified after alignment of spectra.

Original languageEnglish
Title of host publicationAlgorithms in Bioinformatics - 6th International Workshop, WABI 2006, Proceedings
PublisherSpringer Verlag
Number of pages12
ISBN (Print)3540395830, 9783540395836
StatePublished - 2006
Externally publishedYes
Event6th International Workshop on Algorithms in Bioinformatics, WABI 2006 - Zurich, Switzerland
Duration: 11 Sep 200613 Sep 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4175 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference6th International Workshop on Algorithms in Bioinformatics, WABI 2006


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