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Experimental Design for Two-Color Microarrays Applied in a Pre-Existing Split-Plot Experiment
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G. A. Milliken
Published/Copyright:
July 26, 2007
Microarray applications for the study of gene expression are becoming accessible for researchers in more and more systems. Applications from field or laboratory experiments are often complicated by the need to superimpose sample pairing for two-color arrays on experimental designs that may already be complex. For example, split-plot designs are commonly used in biological systems where experiments involve two types of treatments that are not readily applied at the same scale. We demonstrate how effects that are confounded with arrays can still be estimated when there is sufficient replication. To illustrate, we evaluate three methods of sample pairing superimposed on a split-plot design with two treatments, deriving the variance associated with parameter estimates for each. Design A has levels of the whole plot treatment paired on the same microarray within a level of the subplot treatment. Design B has crossed levels paired on the same microarray. Design C has levels of the treatment applied to subplots paired on the same microarray within a whole plot. Designs A and B have lower variance than design C for comparing the levels of the whole plot treatment. Designs B and C have lower variance for comparing the levels of the subplot treatment and design C has lower variance for comparing the levels of the subplot treatment within each level of the whole plot treatment. We provide SAS code for the analyses of variance discussed.
Keywords: ecological genomics; experimental design; gene expression; microarray analysis; split-plot design
Published Online: 2007-7-26
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Keywords for this article
ecological genomics;
experimental design;
gene expression;
microarray analysis;
split-plot design
Articles in the same Issue
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- Accounting for Dependence in Similarity Data from DNA Fingerprinting
- Normalization of Dye Bias in Microarray Data Using the Mixture of Splines Model
- A Generalized Sidak-Holm Procedure and Control of Generalized Error Rates under Independence
- Using Duplicate Genotyped Data in Genetic Analyses: Testing Association and Estimating Error Rates
- Likelihood-Based Inference for Multi-Color Optical Mapping
- Sparse Logistic Regression with Lp Penalty for Biomarker Identification
- Super Learning: An Application to the Prediction of HIV-1 Drug Resistance
- Supervised Detection of Conserved Motifs in DNA Sequences with Cosmo
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- A Bayesian Model of AFLP Marker Evolution and Phylogenetic Inference
- Sequential Quantitative Trait Locus Mapping in Experimental Crosses
- Case-Control Inference of Interaction between Genetic and Nongenetic Risk Factors under Assumptions on Their Distribution
- Inference on the Limiting False Discovery Rate and the P-value Threshold Parameter Assuming Weak Dependence between Gene Expression Levels within Subject
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