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Open AccessJournal ArticleDOI

Optimal factorial designs for cDNA microarray experiments

TLDR
In this paper, the authors consider cDNA microarray experiments when the cell populations have a factorial structure, and investigate the problem of their optimal designing under a baseline parametrization where the objects of interest differ from those under the more common orthogonal parameter.
Abstract
We consider cDNA microarray experiments when the cell populations have a factorial structure, and investigate the problem of their optimal designing under a baseline parametrization where the objects of interest differ from those under the more common orthogonal parametrization. First, analytical results are given for the $2\times 2$ factorial. Since practical applications often involve a more complex factorial structure, we next explore general factorials and obtain a collection of optimal designs in the saturated, that is, most economic, case. This, in turn, is seen to yield an approach for finding optimal or efficient designs in the practically more important nearly saturated cases. Thereafter, the findings are extended to the more intricate situation where the underlying model incorporates dye-coloring effects, and the role of dye-swapping is critically examined.

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Citations
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Journal ArticleDOI

A systematic construction of compromise designs under baseline parameterization

TL;DR: In this article , the authors provide a systematic construction of compromise designs without computer search and without restriction on the run size and the number of factors, and obtain closed-form expressions of the bias and efficiency criteria.
References
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Journal ArticleDOI

Fundamentals of experimental design for cDNA microarrays.

Gary A. Churchill
- 01 Dec 2002 - 
TL;DR: Fundamental issues of how to design an experiment to ensure that the resulting data are amenable to statistical analysis are discussed.
Journal ArticleDOI

Design issues for cDNA microarray experiments.

TL;DR: This paper focuses on microarray experiments, which are used to quantify and compare gene expression on a large scale and can be costly in terms of equipment, consumables and time.
Journal ArticleDOI

Experimental design for gene expression microarrays

TL;DR: In this paper, the authors examined experimental design issues arising with gene expression microarray technology and provided a general set of recommendations for design with microarrays, illustrated in detail for one kind of experimental objective, where they also gave the results of a computer search for good designs.
Journal ArticleDOI

Statistical design and the analysis of gene expression microarray data.

M K Kerr, +1 more
- 01 Feb 2001 - 
TL;DR: This work relates certain features of microarrays to other kinds of experimental data and argues that classical statistical techniques are appropriate and useful and advocate greater attention to experimental design issues and a more prominent role for the ideas of statistical inference in microarray studies.
Journal ArticleDOI

On the utility of pooling biological samples in microarray experiments.

TL;DR: Inference for most genes is not adversely affected by pooling, and it is recommended that pooling be done when fewer than three arrays are used in each condition, and for larger designs, pooling does not significantly improve inferences if few subjects are pooled.
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