Title
Alternative experimental design with an applied normalization scheme can improve statistical power in 2D-DIGE experiments Alternative experimental design with an applied normalization scheme can improve statistical power in 2D-DIGE experiments
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
article
Publication
Subject
Mathematics
Physics
Biology
Source (journal)
Journal of proteome research
Volume/pages
9(2010) :10 , p. 4919-4926
ISSN
1535-3893
ISI
000282257800005
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
2D-DIGE experiments are a high-throughput technique for measuring protein abundances based on gel separation. Traditionally three samples are multiplexed per gel: two biological test samples and a third internal standard sample consisting of a pool of all test samples. We demonstrate that the use of an internal standard helps to account for technical variation caused by spatial intensity biases that exist in the gels and propose a novel data-preprocessing technique, a spatial intensity bias removal (SIBR), which can approximate these biases using only the data of biological replicates loaded on the gel. Using this technique we show that by replacing the internal standard with additional biological replicates, a significant increase in statistical power can be achieved compared to traditional 2D-DIGE designs. This boost in statistical power can be used to reduce the false positive rate for identifying differential protein abundances without compromising sensitivity, or to improve sensitivity without compromising false positive rate. A software implementation of SIBR can be downloaded at http://ibiza.biw.kuleuven.be/SIBR.
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