Accounting for isotopic clustering in Fourier transform mass spectrometry data analysis for clinical diagnostic studies

Kakourou Alexia (), Vach Werner, Nicolardi Simone, Yuri van der Burgt and Mertens Bart
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Kakourou Alexia: Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands
Vach Werner: Center for Medical Biometry and Medical Informatics, University of Freiburg, Freiburg, Germany
Nicolardi Simone: Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
Yuri van der Burgt: Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
Mertens Bart: Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands

Statistical Applications in Genetics and Molecular Biology, 2016, vol. 15, issue 5, 415-430

Abstract: Mass spectrometry based clinical proteomics has emerged as a powerful tool for high-throughput protein profiling and biomarker discovery. Recent improvements in mass spectrometry technology have boosted the potential of proteomic studies in biomedical research. However, the complexity of the proteomic expression introduces new statistical challenges in summarizing and analyzing the acquired data. Statistical methods for optimally processing proteomic data are currently a growing field of research. In this paper we present simple, yet appropriate methods to preprocess, summarize and analyze high-throughput MALDI-FTICR mass spectrometry data, collected in a case-control fashion, while dealing with the statistical challenges that accompany such data. The known statistical properties of the isotopic distribution of the peptide molecules are used to preprocess the spectra and translate the proteomic expression into a condensed data set. Information on either the intensity level or the shape of the identified isotopic clusters is used to derive summary measures on which diagnostic rules for disease status allocation will be based. Results indicate that both the shape of the identified isotopic clusters and the overall intensity level carry information on the class outcome and can be used to predict the presence or absence of the disease.

Keywords: clinical mass-spectrometry based proteomics; Fourier transform mass spectrometry; isotopic distribution; prediction (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1515/sagmb-2016-0005

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