Combination of statistical approaches for analysis of 2-DE data gives complementary results.

Five methods for finding significant changes in proteome data have been used to analyze a two-dimensional gel electrophoresis data set. We used both univariate (ANOVA) and multivariate (Partial Least Squares with jackknife, Cross Model Validation, Power-PLS and CovProc) methods. The gels were taken from a time-series experiment exploring the changes in metabolic enzymes in bovine muscle at five time-points after slaughter. The data set consisted of 1377 protein spots, and for each analysis, the data set were preprocessed to fit the requirements of the chosen method. The generated results were one list from each analysis method of proteins found to be significantly changed according to the experimental design. Although the number of selected variables varied between the methods, we found that this was dependent on the specific aim of each method. CovProc and P-PLS focused more on getting the minimum necessary subset of proteins to explain properties of the samples. These methods ended up with less selected proteins. There was also a correlation between level of significance and frequency of selection for the selected proteins.

[1]  Ulf G. Indahl,et al.  A twist to partial least squares regression , 2005 .

[2]  K. Hollung,et al.  Proteome analysis of early post‐mortem changes in two bovine muscle types: M. longissimus dorsi and M. semitendinosis , 2006, Proteomics.

[3]  Flemming Jessen,et al.  Extracting information from two-dimensional electrophoresis gels by partial least squares regression. , 2002, Proteomics.

[4]  N. Karp,et al.  Application of partial least squares discriminant analysis to two‐dimensional difference gel studies in expression proteomics , 2005, Proteomics.

[5]  Brigitte Picard,et al.  Data analysis methods for detection of differential protein expression in two-dimensional gel electrophoresis. , 2005, Analytical biochemistry.

[6]  Shuguang Huang,et al.  Comparison of false discovery rate methods in identifying genes with differential expression. , 2005, Genomics.

[7]  Geoffrey J McLachlan,et al.  Selection bias in gene extraction on the basis of microarray gene-expression data , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Emilio Marengo,et al.  Application of partial least squares discriminant analysis and variable selection procedures: a 2D-PAGE proteomic study , 2008, Analytical and bioanalytical chemistry.

[9]  Martin H Maurer,et al.  Comparison of statistical approaches for the analysis of proteome expression data of differentiating neural stem cells. , 2005, Journal of proteome research.

[10]  N. Karp,et al.  Experimental and Statistical Considerations to Avoid False Conclusions in Proteomics Studies Using Differential In-gel Electrophoresis*S , 2007, Molecular & Cellular Proteomics.

[11]  K. Hollung,et al.  Proteome changes in bovine longissimus thoracis muscle during the early postmortem storage period. , 2007, Journal of proteome research.

[12]  Harald Martens,et al.  Reducing over-optimism in variable selection by cross-model validation , 2006 .

[13]  Øyvind Langsrud,et al.  Rotation tests , 2005, Stat. Comput..

[14]  John D. Storey.  A direct approach to false discovery rates , 2002 .

[15]  Bo M Jørgensen,et al.  Multivariate data analysis of two-dimensional gel electrophoresis protein patterns from few samples. , 2008, Journal of proteome research.

[16]  H. Martens,et al.  Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR) , 2000 .

[17]  Agnar Höskuldsson,et al.  COVPROC method: strategy in modeling dynamic systems , 2003 .

[18]  M. Rudemo,et al.  Statistical exploration of variation in quantitative two‐dimensional gel electrophoresis data , 2004, Proteomics.

[19]  S. Dudoit,et al.  Multiple Hypothesis Testing in Microarray Experiments , 2003 .

[20]  Agnar Höskuldsson,et al.  The H-principle: New ideas, algorithms and methods in applied mathematics and statistics , 1994 .

[21]  Flemming Jessen,et al.  Multivariate analysis of 2‐DE protein patterns – Practical approaches , 2007, Electrophoresis.

[22]  Berkenbos-Smit.  Statistical data processing in clinical proteomics , 2009 .