Statistical tests for differential expression in cDNA microarray experiments

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, analy...

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Veröffentlicht in:	Genome Biology (Online Edition) 2003-01, Vol.4 (4), p.210-210, Article 210
Hauptverfasser:	Cui, Xiangqin, Churchill, Gary A
Format:	Artikel
Sprache:	eng
Schlagworte:	Analysis Analysis of Variance complementary DNA Gene Expression Profiling - statistics & numerical data gene expression regulation Genes Matched-Pair Analysis microarray technology Oligonucleotide Array Sequence Analysis Review Statistical methods Statistics as Topic t-test Volcanoes
Online-Zugang:	Volltext
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Zusammenfassung:	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.
ISSN:	1474-760X 1465-6906 1474-760X 1465-6914
DOI:	10.1186/gb-2003-4-4-210