Multivariate data analysis of two-dimensional gel electrophoresis protein patterns from few samples

Kristina Nedenskov Jensen, Flemming Jessen, Bo Jørgensen

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

One application of 2D gel electrophoresis is to reveal differences in protein pattern between two or more groups of individuals, attributable to their group membership. Multivariate data analytical methods are useful in pinpointing the spots relevant for discrimination by focusing not only on single spot differences, but on the covariance structure between proteins. However, their outcome is dependent on data scaling, and they may fail in producing valid multivariate models due to the much higher number of "irrelevant" spots present in the gels. The case where only few gels are available and where the aim is to find as many as possible of the group-dependent proteins seems particularly difficult to handle. The present paper investigates such a case regarding the effect of scaling and of prefiltering by univariate nonparametric statistics on the selection of spots. Besides, a modified 'autoscaling' of the full data set based on within-group standard deviations is introduced and shown to be advantageous in revealing potential group-dependent proteins additional to those found by prefiltering.
Original languageEnglish
JournalJournal of Proteome Research
Volume7
Issue number3
Pages (from-to)1288-1296
ISSN1535-3893
DOIs
Publication statusPublished - 2008

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