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Journal of Biomedicine and Biotechnology
Volume 2003, Issue 4, Pages 242-248
Research article

An Automated Peak Identification/Calibration Procedure for High-Dimensional Protein Measures From Mass Spectrometers

1Cancer Prevention Research Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N, MP 702, Seattle, WA 98109-1024, USA
2Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, 700 Olney Road, Norfolk, VA 23507, USA

Received 25 July 2002; Revised 20 December 2002; Accepted 20 December 2002

Copyright © 2003 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Discovery of “signature” protein profiles that distinguish disease states (eg, malignant, benign, and normal) is a key step towards translating recent advancements in proteomic technologies into clinical utilities. Protein data generated from mass spectrometers are, however, large in size and have complex features due to complexities in both biological specimens and interfering biochemical/physical processes of the measurement procedure. Making sense out of such high-dimensional complex data is challenging and necessitates the use of a systematic data analytic strategy. We propose here a data processing strategy for two major issues in the analysis of such mass-spectrometry-generated proteomic data: (1) separation of protein “signals” from background “noise” in protein intensity measurements and (2) calibration of protein mass/charge measurements across samples. We illustrate the two issues and the utility of the proposed strategy using data from a prostate cancer biomarker discovery project as an example.