Journal Article

Comparison of algorithms for pre-processing of SELDI-TOF mass spectrometry data

Alejandro Cruz-Marcelo, Rudy Guerra, Marina Vannucci, Yiting Li, Ching C. Lau and Tsz-Kwong Man

in Bioinformatics

Volume 24, issue 19, pages 2129-2136
Published in print October 2008 | ISSN: 1367-4803
Published online August 2008 | e-ISSN: 1460-2059 | DOI:
Comparison of algorithms for pre-processing of SELDI-TOF mass spectrometry data

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Motivation: Surface-enhanced laser desorption and ionization (SELDI) time of flight (TOF) is a mass spectrometry technology. The key features in a mass spectrum are its peaks. In order to locate the peaks and quantify their intensities, several pre-processing steps are required. Though different approaches to perform pre-processing have been proposed, there is no systematic study that compares their performance.

Results: In this article, we present the results of a systematic comparison of various popular packages for pre-processing of SELDI-TOF data. We evaluate their performance in terms of two of their primary functions: peak detection and peak quantification. Regarding peak quantification, the performance of the algorithms is measured in terms of reproducibility. For peak detection, the comparison is based on sensitivity and false discovery rate. Our results show that for spectra generated with low laser intensity, the software developed by Ciphergen Biosystems (ProteinChip© Software 3.1 with the additional tool Biomarker Wizard) produces relatively good results for both peak quantification and detection. On the other hand, for the data produced with either medium or high laser intensity, none of the methods show uniformly better performances under both criteria. Our analysis suggests that an advantageous combination is the use of the packages MassSpecWavelet and PROcess, the former for peak detection and the latter for peak quantification.


Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6252 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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