Open Access Research

A comparison of imputation procedures and statistical tests for the analysis of two-dimensional electrophoresis data

Jeffrey C Miecznikowski14*, Senthilkumar Damodaran2, Kimberly F Sellers3 and Richard A Rabin2

Author Affiliations

1 Department of Biostatistics; University at Buffalo, Buffalo, NY 14214 USA

2 Department of Pharmacology and Toxicology; School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14214 USA

3 Department of Mathematics and Statistics; Georgetown University, Washington, DC 20057 USA

4 Department of Biostatistics; Roswell Park Cancer Institute, Buffalo, NY 14263 USA

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Proteome Science 2010, 8:66  doi:10.1186/1477-5956-8-66

Published: 15 December 2010



Numerous gel-based softwares exist to detect protein changes potentially associated with disease. The data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. A particularly important topic is how the various softwares handle missing data. To date, no one has extensively studied the impact that interpolating missing data has on subsequent analysis of protein spots.


This work highlights the existing algorithms for handling missing data in two-dimensional gel analysis and performs a thorough comparison of the various algorithms and statistical tests on simulated and real datasets. For imputation methods, the best results in terms of root mean squared error are obtained using the least squares method of imputation along with the expectation maximization (EM) algorithm approach to estimate missing values with an array covariance structure. The bootstrapped versions of the statistical tests offer the most liberal option for determining protein spot significance while the generalized family wise error rate (gFWER) should be considered for controlling the multiple testing error.


In summary, we advocate for a three-step statistical analysis of two-dimensional gel electrophoresis (2-DE) data with a data imputation step, choice of statistical test, and lastly an error control method in light of multiple testing. When determining the choice of statistical test, it is worth considering whether the protein spots will be subjected to mass spectrometry. If this is the case a more liberal test such as the percentile-based bootstrap t can be employed. For error control in electrophoresis experiments, we advocate that gFWER be controlled for multiple testing rather than the false discovery rate.