Journal ArticleDOI
Fundamentals of experimental design for cDNA microarrays.
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 wellread more
Citations
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Journal ArticleDOI
Microarray data normalization and transformation
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
Sorin Draghici,Purvesh Khatri,Adi L. Tarca,Kashyap Amin,Arina Done,Calin Voichita,Constantin Georgescu,Roberto Romero +7 more
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,Gary A. Churchill +1 more
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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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
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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