Exception Discovery: A Novel Method for the Identification of Differentially Expressed Proteins

The identification of differentially expressed proteins (DEPs) observed under specific conditions is one of the key issues in proteomics research. There are currently several ways to detect the changes of a specific protein's expression level in two-dimensional electrophoresis (2-DE) gel images such as statistical analysis and graphical visualization. However, it is quite difficult to handle the information of an individual protein manually by these methods due to the large distortions of patterns in 2-DE images. This paper proposes a method of analyzing DEPs for a specific disease. In order to automatically extract meaningful DEPs in a set of 2-DE gel images, we have designed an exception function that is suitable to measure the anomalous change of the expression level of an individual protein. We present the comparison results of the proposed method versus a Wilcoxon paired t -test that is one of the widely used statistical analysis methods. Several experiments are performed to address not only the effectiveness of the exception function but also the fact that these two methods can compensate each other practically.

[1]  A. Görg,et al.  The current state of two‐dimensional electrophoresis with immobilized pH gradients , 2000, Electrophoresis.

[2]  S. Dudoit,et al.  Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. , 2002, Nucleic acids research.

[3]  Ingrid Lönnstedt.  Replicated microarray data , 2001 .

[4]  H. Lindman.  Analysis of variance in complex experimental designs , 1974 .

[5]  M Vingron,et al.  Identification and Classification of Differentially Expressed Genes in Renal Cell Carcinoma by Expression Profiling on a Global Human 31 , 500-Element cDNA Array , 2001 .

[6]  S Veeser,et al.  Multiresolution image registration for two‐dimensional gel electrophoresis , 2001, Proteomics.

[7]  A. Shevchenko,et al.  Mass spectrometric sequencing of proteins silver-stained polyacrylamide gels. , 1996, Analytical chemistry.

[8]  R. Tibshirani,et al.  Clustering methods for the analysis of DNA microarray data , 1999 .

[9]  R. Peters,et al.  Tumors of the Liver and Intrahepatic Bile Ducts , 1990 .

[10]  D. Arnott,et al.  An integrated approach to proteome analysis: identification of proteins associated with cardiac hypertrophy. , 1998, Analytical biochemistry.

[11]  Nam-Gyun Kim,et al.  Proteomic analysis and molecular characterization of tissue ferritin light chain in hepatocellular carcinoma , 2002, Hepatology.

[12]  M. Mann,et al.  Analysis of proteins and proteomes by mass spectrometry. , 2001, Annual review of biochemistry.

[13]  Pierre Baldi,et al.  A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes , 2001, Bioinform..

[14]  Randall G. Lee,et al.  Tumors of the liver and intrahepatic bile ducts , 2003, American Journal of Gastroenterology.

[15]  J. Klein,et al.  Two-dimensional gel electrophoresis: a fundamental tool for expression proteomics studies. , 2004, Contributions to nephrology.

[16]  Sung Gyoo Park,et al.  Proteome analysis of hepatocellular carcinoma. , 2002, Biochemical and biophysical research communications.

[17]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[18]  P. Broberg.  Statistical methods for ranking differentially expressed genes , 2003, Genome Biology.