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

Proteome Science

Research

Jeffrey C Miecznikowski¹  Kimberly F Sellers²  Senthilkumar Damodaran³  Richard A Rabin³ 

[1] Department of Biostatistics, University at Buffalo, 14214, Buffalo, NY, USA;Department of Biostatistics, Roswell Park Cancer Institute, 14263, Buffalo, NY, USA;Department of Mathematics and Statistics, Georgetown University, 20057, Washington, DC, USA;Department of Pharmacology and Toxicology; School of Medicine and Biomedical Sciences, University at Buffalo, 14214, Buffalo, NY, USA;

关键词: Root Mean Square Error;    Protein Spot;    Imputation Method;    Complete Dataset;    Impute Dataset;   

DOI  :  10.1186/1477-5956-8-66

 received in 2010-06-17, accepted in 2010-12-15,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundNumerous 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.ResultsThis 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.ConclusionsIn 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.

【 授权许可】

CC BY   
© Miecznikowski et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.