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Explorative data analysis of two‐dimensional electrophoresis gels
Author(s) -
Schultz Jakob,
Gottlieb David Mark,
Petersen Marianne,
Nesic Ljiljana,
Jacobsen Susanne,
Søndergaard Ib
Publication year - 2004
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.200305715
Subject(s) - principal component analysis , multivariate statistics , transformation (genetics) , biological system , computer science , volume (thermodynamics) , pattern recognition (psychology) , electrophoresis , chromatography , pattern analysis , two dimensional gel electrophoresis , data mining , artificial intelligence , chemistry , biology , machine learning , physics , biochemistry , quantum mechanics , proteomics , gene
Methods for classification of two‐dimensional (2‐DE) electrophoresis gels based on multivariate data analysis are demonstrated. Two‐dimensional gels of ten wheat varieties are analyzed and it is demonstrated how to classify the wheat varieties in two qualities and a method for initial screening of gels is presented. First, an approach is demonstrated in which no prior knowledge of the separated proteins is used. Alignment of the gels followed by a simple transformation of data makes it possible to analyze the gels in an automated explorative manner by principal component analysis, to determine if the gels should be further analyzed. A more detailed approach is done by analyzing spot volume lists by principal components analysis and partial least square regression. The use of spot volume data offers a mean to investigate the spot pattern and link the classified protein patterns to distinct spots on the gels for further investigation. The explorative approach in analysis of 2‐D gels makes it possible, in a fast and convenient way, to screen many gels in order to determine the protein patterns that form clusters and could be selected for further examination.