Statistical tests for differential expression in cDNA microarray experiments
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Open Access
- 1 April 2003
- journal article
- Published by Springer Nature in Genome Biology
- Vol. 4 (4), 210
- https://doi.org/10.1186/gb-2003-4-4-210
Abstract
Extracting biological information from microarray data requires appropriate statistical methods. The simplest statistical method for detecting differential expression is the t test, which can be used to compare two conditions when there is replication of samples. With more than two conditions, 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.Keywords
This publication has 26 references indexed in Scilit:
- Variation in gene expression within and among natural populationsNature Genetics, 2002
- A Direct Approach to False Discovery RatesJournal of the Royal Statistical Society Series B: Statistical Methodology, 2002
- A Model for Measurement Error for Gene Expression ArraysJournal of Computational Biology, 2001
- Assessing Gene Significance from cDNA Microarray Expression Data via Mixed ModelsJournal of Computational Biology, 2001
- The control of the false discovery rate in multiple testing under dependencyThe Annals of Statistics, 2001
- Computational analysis of microarray dataNature Reviews Genetics, 2001
- Significance analysis of microarrays applied to the ionizing radiation responseProceedings of the National Academy of Sciences, 2001
- Analysis of Variance for Gene Expression Microarray DataJournal of Computational Biology, 2000
- Global Gene Expression Profiling in Escherichia coliK12Journal of Biological Chemistry, 2000
- Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic ScaleScience, 1997