Literature DB >> 12724289

Statistical design of reverse dye microarrays.

K Dobbin1, J H Shih, R Simon.   

Abstract

MOTIVATION: In cDNA microarray experiments all samples are labelled with either Cy3 dye or Cy5 dye. Certain genes exhibit dye bias-a tendency to bind more efficiently to one of the dyes. The common reference design avoids the problem of dye bias by running all arrays 'forward', so that the samples being compared are always labelled with the same dye. But comparison of samples labelled with different dyes is sometimes of interest. In these situations, it is necessary to run some arrays 'reverse'-with the dye labelling reversed-in order to correct for the dye bias. The design of these experiments will impact one's ability to identify genes that are differentially expressed in different tissues or conditions. We address the design issue of how many specimens are needed, how many forward and reverse labelled arrays to perform, and how to optimally assign Cy3 and Cy5 labels to the specimens.
RESULTS: We consider three types of experiments for which some reverse labelling is needed: paired samples, samples from two predefined groups, and reference design data when comparison with the reference is of interest. We present simple probability models for the data, derive optimal estimators for relative gene expression, and compare the efficiency of the estimators for a range of designs. In each case, we present the optimal design and sample size formulas. We show that reverse labelling of individual arrays is generally not required.

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Year:  2003        PMID: 12724289     DOI: 10.1093/bioinformatics/btg076

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  26 in total

1.  Identification and handling of artifactual gene expression profiles emerging in microarray hybridization experiments.

Authors:  Leonid Brodsky; Andrei Leontovich; Michael Shtutman; Elena Feinstein
Journal:  Nucleic Acids Res       Date:  2004-03-03       Impact factor: 16.971

2.  Intensity-based analysis of two-colour microarrays enables efficient and flexible hybridization designs.

Authors:  Peter A C 't Hoen; Rolf Turk; Judith M Boer; Ellen Sterrenburg; Renée X de Menezes; Gert-Jan B van Ommen; Johan T den Dunnen
Journal:  Nucleic Acids Res       Date:  2004-02-24       Impact factor: 16.971

Review 3.  Statistical issues in the design and analysis of gene expression microarray studies of animal models.

Authors:  Lisa M McShane; Joanna H Shih; Aleksandra M Michalowska
Journal:  J Mammary Gland Biol Neoplasia       Date:  2003-07       Impact factor: 2.673

4.  Optimal allocation in designs for assessing heterosis from cDNA gene expression data.

Authors:  Hans-Peter Piepho
Journal:  Genetics       Date:  2005-06-14       Impact factor: 4.562

Review 5.  A review of statistical methods for expression quantitative trait loci mapping.

Authors:  Christina Kendziorski; Ping Wang
Journal:  Mamm Genome       Date:  2006-06-12       Impact factor: 2.957

6.  A discussion of statistical methods for design and analysis of microarray experiments for plant scientists.

Authors:  Dan Nettleton
Journal:  Plant Cell       Date:  2006-09       Impact factor: 11.277

7.  Valosin-containing protein (p97) is a regulator of endoplasmic reticulum stress and of the degradation of N-end rule and ubiquitin-fusion degradation pathway substrates in mammalian cells.

Authors:  Cezary Wójcik; Maga Rowicka; Andrzej Kudlicki; Dominika Nowis; Elizabeth McConnell; Marek Kujawa; George N DeMartino
Journal:  Mol Biol Cell       Date:  2006-08-16       Impact factor: 4.138

8.  Gene expression profiling of Burkholderia cenocepacia at the time of cepacia syndrome: loss of motility as a marker of poor prognosis?

Authors:  Lucie Kalferstova; Michal Kolar; Libor Fila; Jolana Vavrova; Pavel Drevinek
Journal:  J Clin Microbiol       Date:  2015-02-18       Impact factor: 5.948

9.  Designing toxicogenomics studies that use DNA array technology.

Authors:  Robert R Delongchamp; Cruz Velasco; Varsha G Desai; Taewon Lee; James C Fuscoe
Journal:  Bioinform Biol Insights       Date:  2008-08-14

10.  Image analysis and data normalization procedures are crucial for microarray analyses.

Authors:  Ali Kpatcha Kadanga; Christine Leroux; Muriel Bonnet; Stéphanie Chauvet; Bruno Meunier; Isabelle Cassar-Malek; Jean-François Hocquette
Journal:  Gene Regul Syst Bio       Date:  2008-03-17
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