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Journal ArticleDOI

Fundamentals of experimental design for cDNA microarrays.

Gary A. Churchill
- 01 Dec 2002 - 
- Vol. 32, Iss: 4, pp 490-495
TLDR
Fundamental issues of how to design an experiment to ensure that the resulting data are amenable to statistical analysis are discussed.
Abstract
Microarray technology is now widely available and is being applied to address increasingly complex scientific questions Consequently, there is a greater demand for statistical assessment of the conclusions drawn from microarray experiments This review discusses fundamental issues of how to design an experiment to ensure that the resulting data are amenable to statistical analysis The discussion focuses on two-color spotted cDNA microarrays, but many of the same issues apply to single-color gene-expression assays as well

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Citations
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Journal ArticleDOI

Microarray data normalization and transformation

John Quackenbush
- 01 Dec 2002 - 
TL;DR: This review focuses on the much more mundane but indispensable tasks of 'normalizing' data from individual hybridizations to make meaningful comparisons of expression levels, and of 'transforming' them to select genes for further analysis and data mining.
Journal ArticleDOI

Microarray data analysis: from disarray to consolidation and consensus.

TL;DR: In just a few years, microarrays have gone from obscurity to being almost ubiquitous in biological research, and points of consensus are emerging about the general approaches that warrant use and elaboration.
Journal ArticleDOI

A systems biology approach for pathway level analysis

TL;DR: An impact analysis is developed that includes the classical statistics but also considers other crucial factors such as the magnitude of each gene's expression change, their type and position in the given pathways, their interactions, etc.
Journal ArticleDOI

Experimental design

TL;DR: Experimental design is reviewed here for broad classes of data collection and analysis problems, including: fractioning techniques based on orthogonal arrays, Latin hypercube designs and their variants for computer experimentation, efficient design for data mining and machine learning applications, and sequential design for active learning.
Journal ArticleDOI

Statistical tests for differential expression in cDNA microarray experiments.

Xiangqin Cui, +1 more
- 17 Mar 2003 - 
TL;DR: Analysis of variance (ANOVA) can be used, and the mixed ANOVA model is a general and powerful approach for microarray experiments with multiple factors and/or several sources of variation.
References
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Book

The Design of Experiments

R. A. Fisher
Journal ArticleDOI

Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation

TL;DR: This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments.
Journal ArticleDOI

Microarray data normalization and transformation

John Quackenbush
- 01 Dec 2002 - 
TL;DR: This review focuses on the much more mundane but indispensable tasks of 'normalizing' data from individual hybridizations to make meaningful comparisons of expression levels, and of 'transforming' them to select genes for further analysis and data mining.
Journal ArticleDOI

Genetic dissection of transcriptional regulation in budding yeast.

TL;DR: To begin to understand the genetic architecture of natural variation in gene expression, genetic linkage analysis of genomewide expression patterns in a cross between a laboratory strain and a wild strain of Saccharomyces cerevisiae was carried out.
Journal ArticleDOI

Analysis of Variance for Gene Expression Microarray Data

TL;DR: It is demonstrated that ANOVA methods can be used to normalize microarray data and provide estimates of changes in gene expression that are corrected for potential confounding effects and establishes a framework for the general analysis and interpretation of micro array data.
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