%0 Journal Article
%T A comparison of imputation procedures and statistical tests for the analysis of two-dimensional electrophoresis data
%A Jeffrey C Miecznikowski
%A Senthilkumar Damodaran
%A Kimberly F Sellers
%A Richard A Rabin
%J Proteome Science
%D 2010
%I BioMed Central
%R 10.1186/1477-5956-8-66
%X 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.Analysis of quantitative changes in a specific proteome (i.e., complement of proteins expressed in a particular tissue or cell at a given time) is commonly carried out using two-dimensional gel electrophoresis (2-DE). With this procedure, proteins are separated in the first dimension based on iso-electric point, followed by separation based on molecular mass in the second dimension. Subsequently, protein spots are visualized, and the scanned gel images are analyzed using image analysis programs (e.g. ImageMaster, PDQuest). Once the relevant proteins spots have been determined, these specific proteins are identified using mass spectrometry. Because quantit
%U http://www.proteomesci.com/content/8/1/66