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Processing of Data Generated by 2-Dimensional Gel Electrophoresis for Statistical Analysis: Missing Data, Normalization, and Statistics

Jinsook Chang,^‡ Holly Van Remmen,^†^# Walter F. Ward,^‡ Fred E. Regnier, Arlan Richardson,*^†^# and John Cornell^§^#

Departments of Cellular and Structural Biology, Physiology, and, Medicine, and the Barshop Center for Longevity Studies, University of Texas Health Science Center at San Antonio, San Antonio, Texas 78284-7762, South Texas Veterans Health Care System, San Antonio, Texas 78284-7762, and Department of Chemistry, Purdue University, West Lafayette, Indiana 47907

J. Proteome Res., 2004, 3 (6), pp 1210–1218

DOI: 10.1021/pr049886m

Publication Date (Web): October 9, 2004

‡

Department of Physiology, University of Texas Health Science Center at San Antonio.

†

Department of Cellular and Structural Biology, University of Texas Health Science Center at San Antonio.

Barshop Center for Longevity Studies, University of Texas Health Science Center at San Antonio.

GRECC South Texas Veterans Health Care System.

Department of Chemistry, Purdue University.

To whom correspondence should be addressed. Fax: (210) 567-4423. E-mail: cornell@uthscsa.edu.

Center for Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio.

Abstract

Several high-throughput statistical methods were evaluated for processing data generated by two-dimensional polyacrylamide gel electrophoresis, including how to handle missing data, normalization, and statistical analysis of data obtained from 2-D gels. Quantile normalization combined with a nonparametric permutation test based on minimizing false discover rates gave the highest yield of proteins that changed with genotype and detected the anticipated 50% decrease in Mn-superoxide dismutase (MnSOD) protein levels in mitochondrial extracts obtained from MnSOD-deficient mice.

Keywords: 2-D PAGE • missing data • normalization • mitochondria • permutation test • false discovery rates

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