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Multivariate analysis of 2‐DE protein patterns – Practical approaches
Author(s) -
Jacobsen Susanne,
Grove Harald,
Nedenskov Jensen Kristina,
Sørensen Helle A.,
Jessen Flemming,
Hollung Kristin,
Uhlen Anne Kjersti,
Jørgensen Bo M.,
Færgestad Ellen Mosleth,
Søndergaard Ib
Publication year - 2007
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.200600414
Subject(s) - multivariate statistics , multivariate analysis , kernel (algebra) , set (abstract data type) , selection (genetic algorithm) , matching (statistics) , quality (philosophy) , computer science , mathematics , statistics , pattern recognition (psychology) , artificial intelligence , philosophy , epistemology , combinatorics , programming language
Abstract Practical approaches to the use of multivariate data analysis of 2‐DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2‐DE data. Four wheat varieties were selected on the basis of their baking quality. Two of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2‐DEs. Patterns related to the wheat varieties, harvest times and quality were detected on images of 2‐DE protein patterns for all the three strategies. The use of the multivariate methods was evaluated in the alignment and matching procedures of 2‐DE gels. All the three strategies were able to discriminate the samples according to quality, harvest time and variety, although different subsets of protein spots were selected. The explorative approach of using multivariate data analysis and variable selection in the analyses of 2‐DEs seems to be promising as a fast, reliable and convenient way of screening and transforming many gel images into spot quantities.