Literature DB >> 22499679

Optimality criteria for the design of 2-color microarray studies.

Kathleen F Kerr1.   

Abstract

We discuss the definition and application of design criteria for evaluating the efficiency of 2-color microarray designs. First, we point out that design optimality criteria are defined differently for the regression and block design settings. This has caused some confusion in the literature and warrants clarification. Linear models for microarray data analysis have equivalent formulations as ANOVA or regression models. However, this equivalence does not extend to design criteria. We discuss optimality criterion, and argue against applying regression-style D-optimality to the microarray design problem. We further disfavor E- and D-optimality (as defined in block design) because they are not attuned to scientific questions of interest.

Mesh:

Year:  2012        PMID: 22499679      PMCID: PMC3979428          DOI: 10.1515/1544-6115.1583

Source DB:  PubMed          Journal:  Stat Appl Genet Mol Biol        ISSN: 1544-6115


  11 in total

1.  Assessing gene significance from cDNA microarray expression data via mixed models.

Authors:  R D Wolfinger; G Gibson; E D Wolfinger; L Bennett; H Hamadeh; P Bushel; C Afshari; R S Paules
Journal:  J Comput Biol       Date:  2001       Impact factor: 1.479

2.  Analysis of variance for gene expression microarray data.

Authors:  M K Kerr; M Martin; G A Churchill
Journal:  J Comput Biol       Date:  2000       Impact factor: 1.479

3.  Comparison of microarray designs for class comparison and class discovery.

Authors:  K Dobbin; R Simon
Journal:  Bioinformatics       Date:  2002-11       Impact factor: 6.937

4.  Experimental design for gene expression microarrays.

Authors:  M K Kerr; G A Churchill
Journal:  Biostatistics       Date:  2001-06       Impact factor: 5.899

Review 5.  Design issues for cDNA microarray experiments.

Authors:  Yee Hwa Yang; Terry Speed
Journal:  Nat Rev Genet       Date:  2002-08       Impact factor: 53.242

Review 6.  Linear models for microarray data analysis: hidden similarities and differences.

Authors:  M Kathleen Kerr
Journal:  J Comput Biol       Date:  2003       Impact factor: 1.479

7.  On the A-optimality criterion for finding two-color microarray optimal designs.

Authors:  Frans E S Tan; Valéria Lima Passos
Journal:  J Comput Biol       Date:  2011-01-08       Impact factor: 1.479

8.  Linear models and empirical bayes methods for assessing differential expression in microarray experiments.

Authors:  Gordon K Smyth
Journal:  Stat Appl Genet Mol Biol       Date:  2004-02-12

9.  Optimal designs for 2-color microarray experiments.

Authors:  P S Sanchez; G F V Glonek
Journal:  Biostatistics       Date:  2009-04-28       Impact factor: 5.899

10.  MicroarrayDesigner: an online search tool and repository for near-optimal microarray experimental designs.

Authors:  Ahmet Sacan; Nilgun Ferhatosmanoglu; Hakan Ferhatosmanoglu
Journal:  BMC Bioinformatics       Date:  2009-09-22       Impact factor: 3.169

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