Microarray data quality analysis: lessons from the AFGC project

被引:58
|
作者
Finkelstein, D
Ewing, R
Gollub, J
Sterky, F
Cherry, JM
Somerville, S
机构
[1] Carnegie Inst Washington, Dept Plant Biol, Stanford, CA 94305 USA
[2] Stanford Univ, Dept Genet, Stanford, CA 94305 USA
关键词
Arabidopsis; annotation; microarray functional genomics; normalization;
D O I
10.1023/A:1013765922672
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Genome-wide expression profiling with DNA microarrays has and will provide a great deal of data to the plant scientific community. However, reliability concerns have required the development data quality tests for common systematic biases. Fortunately, most large-scale systematic biases are detectable and some are correctable by normalization. Technical replication experiments and statistical surveys indicate that these biases vary widely in severity and appearance. As a result, no single normalization or correction method currently available is able to address all the issues. However, careful sequence selection, array design, experimental design and experimental annotation can substantially improve the quality and biological of microarray data. In this review, we discuss these issues with reference to examples from the Arabidopsis Functional Genomics Consortium (AFGC) microarray project.
引用
收藏
页码:119 / 131
页数:13
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