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Bioinformatics Advance Access published online on March 27, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl106
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received January 31, 2006
Revised March 7, 2006
Accepted March 18, 2006

Article

Finding regions of significance in SELDI measurements for identifying protein biomarkers

Chuen Seng Tan 1, Alexander Ploner 2, Andreas Quandt 2, Janne Lehtiö 3, and Yudi Pawitan 2 *

1 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Center for Molecular Epidemiology, Yong Loo Lin School of Medicine, National University of Singapore and Genome Institute of Singapore, Singapore
2 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
3 Cancer Centrum Karolinska, Karolinska Institutet, Stockholm, Sweden; Clinical Proteomics, Karolinska Biomics Center, Karolinska University Hospital, Stockholm, Sweden

* To whom correspondence should be addressed.
Yudi Pawitan, E-mail: yudi.pawitan{at}ki.se


  Abstract

Motivation: There is a well-recognized potential of protein expression profiling using the surface-enhanced laser desorption and ionization (SELDI) technology for discovering biomarkers that can be applied in clinical diagnosis, prognosis and therapy prediction. The pre-processing of the raw data, however, is still problematic.

Methods: We focus on the peak detection step, where the standard method is marked by poor specificity. Currently, scientists need to inspect individual spectra visually and laboriously in order to verify that spectral peaks identified by the standard method are real. Motivated by this multi-spectral process, we investigate an analytical approach - called RS bluefor ‘regions of significance’ - that reduces the data to a single spectrum of F-statistics capturing significant variability between spectra. To account for multiple testing, we use a false discovery rate (FDR) criterion for identifying potentially interesting proteins.

Results: We show that RS has better operating characteristics than several existing methods, and demonstrate routine applications on a number of large datasets.

Availability: RS is implemented in an R package called ProSpect which is available at http://www.meb.ki.se/~yudpaw.

Associate Editor: Martin Bishop
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