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Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: assessing experimental effects in a melanoma cell study
Author(s) -
Elmer A. Ferná ndez,
María Romina Girotti,
Juan Antonio López,
Andrea S. Llera,
Osvaldo L. Podhajcer,
R. J. C. Cantet,
Mónica Balzarini
Publication year - 2008
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btn508
Subject(s) - skewness , normalization (sociology) , difference gel electrophoresis , heteroscedasticity , linear model , biological system , covariate , studentized residual , statistics , econometrics , computer science , chemistry , mathematics , biology , proteomics , biochemistry , sociology , anthropology , gene
Difference in-gel electrophoresis (DIGE)-based protein expression analysis allows assessing the relative expression of proteins in two biological samples differently labeled (Cy5, Cy3 CyDyes). In the same gel, a reference sample is also used (Cy2 CyDye) for spot matching during image analysis and volume normalization. The standard statistical techniques to identify differentially expressed (DE) proteins are the calculation of fold-changes and the comparison of treatment means by the t-test. The analyses rarely accounts for other experimental effects, such as CyDye and gel effects, which could be important sources of noise while detecting treatment effects.

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