Data Analysis of Two Dimensional Electrophoresis Gels by Multivariate Statistical Methods

In recent years technology has emerged to describe the protein spots on two-dimensional gel electrophoresis. Numerous gel statistical analysis software has been developed and the impact of these initiatives on promoting proteomics is impressive. Proteomic analysis of massive data sets with having high variable need multivariate methods to provide the simultaneous analysis of multiple variables. In this study، the process of cellular differentiation and aging of stem cells to astrocytes studied by proteomics and gel analysis software and statistical applications were considered.

Proteome of four groups (stem cells، young astrocytes، moderately differentiated astrocytes and old astrocytes) were analyzed by the software of Progenesis Same Spots. Cluster analysis، principal component analysis and analysis of power were used in the experimental groups.

In bioinformatics and statistical analysis of two-dimensional gel electrophoresis technique were detected 940 protein spots with significant expression changes (p <0. 05) in four groups that comparisons between groups suggest that the expression of new proteins and the silencing of certain proteins in the signaling pathway of cell differentiation and senescence. Clustering analysis of the expression of proteins can be divided into two main clusters indicate that there are clusters of proteins with similar expression which these proteins can provide similar performance in terms of testing or indicate its presence in the same biological pathway. PCA analysis confirmed the clustering results showed that the protein has been classified according to the test conditions. Finally، we can conclude that using statistical analysis software to quickly and easily done and significant expression changes induced differentiation and senescence on proteome level as well as evaluated with statistical analysis of clustering and PCA and determined indicators of changes.