a statistical model to identify differentially expressed proteins in 2d page gels一个统计模型来识别差异表达蛋白质凝胶2 d页面.pdfVIP

A Statistical Model to Identify Differentially Expressed Proteins in 2D PAGE Gels 1,2 3 4 2 1,2 Steven H. Wu , Michael A. Black , Robyn A. North , Kelly R. Atkinson , Allen G. Rodrigo * 1 Bioinformatics Institute, University of Auckland, Auckland, New Zealand, 2 School of Biological Sciences, University of Auckland, Auckland, New Zealand, 3 Department of Biochemistry, University of Otago, Dunedin, New Zealand, 4 Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand Abstract Two dimensional polyacrylamide gel electrophoresis (2D PAGE) is used to identify differentially expressed proteins and may be applied to biomarker discovery. A limitation of this approach is the inability to detect a protein when its concentration falls below the limit of detection. Consequently, differential expression of proteins may be missed when the level of a protein in the cases or controls is below the limit of detection for 2D PAGE. Standard statistical techniques have difficulty dealing with undetected proteins. To address this issue, we propose a mixture model that takes into account both detected and non-detected proteins. Non-detected proteins are classified either as (a) proteins that are not expressed in at least one replicate, or (b) proteins that are expressed but are below the limit of detection. We obtain maximum likelihood estimates of the parameters of the mixture model, including the group-specific probability of expression and mean expression intensities. Differentially expressed proteins can be detected by using a Likelihood Ratio Test (LRT). Our simulation results, using data generated from biological experiments,