Linear Models for Microarray Data Analysis: Hidden Similarities and Differences
- 1 December 2003
- journal article
- research article
- Published by Mary Ann Liebert Inc in Journal of Computational Biology
- Vol. 10 (6) , 891-901
- https://doi.org/10.1089/106652703322756131
Abstract
In the past several years many linear models have been proposed for analyzing two-color microarray data. As presented in the literature, many of these models appear dramatically different. However, many of these models are reformulations of the same basic approach to analyzing microarray data. This paper demonstrates the equivalence of some of these models. Attention is directed at choices in microarray data analysis that have a larger impact on the results than the choice of linear model.Keywords
This publication has 15 references indexed in Scilit:
- DNA Microarray Experiments: Biological and Technological AspectsBiometrics, 2002
- Fundamentals of experimental design for cDNA microarraysNature Genetics, 2002
- Models for microarray gene expression dataJournal of Biopharmaceutical Statistics, 2002
- Empirical Bayes Analysis of a Microarray ExperimentJournal of the American Statistical Association, 2001
- The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogasterNature Genetics, 2001
- Experimental design for gene expression microarraysBiostatistics, 2001
- Microarray Expression Profiling Identifies Genes with Altered Expression in HDL-Deficient MiceGenome Research, 2000
- Analysis of Variance for Gene Expression Microarray DataJournal of Computational Biology, 2000
- Large-Scale Monitoring of Host Cell Gene Expression during HIV-1 Infection Using cDNA MicroarraysVirology, 2000
- R: A Language for Data Analysis and GraphicsJournal of Computational and Graphical Statistics, 1996