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Biostatistics Advance Access originally published online on March 26, 2009
Biostatistics 2009 10(3):481-500; doi:10.1093/biostatistics/kxp006
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© The Author 2009. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

An insight into high-resolution mass-spectrometry data

J. E. Eckel-passow*, A. L. Oberg and T. M. Therneau

Division of Biomedical Statistics & Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN 55905, USA
eckelpassow.jeanette{at}mayo.edu

H. R. Bergen, III

Mayo Proteomics Research Center, Mayo Clinic College of Medicine, Rochester, MN 55905, USA

* To whom correspondence should be addressed.

Mass spectrometry is a powerful tool with much promise in global proteomic studies. The discipline of statistics offers robust methodologies to extract and interpret high-dimensional mass-spectrometry data and will be a valuable contributor to the field. Here, we describe the process by which data are produced, characteristics of the data, and the analytical preprocessing steps that are taken in order to interpret the data and use it in downstream statistical analyses. Because of the complexity of data acquisition, statistical methods developed for gene expression microarray data are not directly applicable to proteomic data. Areas in need of statistical research for proteomic data include alignment, experimental design, abundance normalization, and statistical analysis.

Keywords: Experimental design; Fourier transform; Mass calibration; Mass spectrometry; Normalization

Received March 16, 2007; revised March 12, 2008; revised December 2, 2008; revised January 20, 2009; accepted for publication February 23, 2009.


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