Analyzing factorial designed microarray experiments

DOI10.1016/j.jmva.2004.02.004zbMath1047.62113OpenAlexW1977481949MaRDI QIDQ1876978

Penelope L. Miron, Alexander Miron, Faisal M. Merchant, Arden Miller, J. Dirk Iglehart, Robert Gentleman, Denise M. Scholtens

Publication date: 16 August 2004

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2004.02.004

zbMATH Keywords

Experimental designGene expressionLinear modelGene selectionEstrogen target

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Biochemistry, molecular biology (92C40) Factorial statistical designs (62K15) Genetics and epigenetics (92D10)

Related Items (3)

Estimating the proportion of true null hypotheses using the pattern of observedp-values ⋮ Shrinkage estimation of effect sizes as an alternative to hypothesis testing followed by estimation in high-dimensional biology: applications to differential gene expression ⋮ Bayesian modeling of factorial time-course data with applications to a bone aging gene expression study

Uses Software

• R
• sma

Cites Work

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• Multiple hypothesis testing in microarray experiments.
• The control of the false discovery rate in multiple testing under dependency.
• Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection
• Statistical modeling of large microarray data sets to identify stimulus-response profiles
• Resistant and Test-Based Outlier Rejection: Effects on Gaussian One- and Two-Sample Inference
• Bayesian Models for Gene Expression With DNA Microarray Data
• Experimental design for gene expression microarrays
• Factorial and time course designs for cDNA microarray experiments

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