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实验室操作中的随机化是获得可重复微阵列结果的关键。

Randomization in laboratory procedure is key to obtaining reproducible microarray results.

作者信息

Yang Hyuna, Harrington Christina A, Vartanian Kristina, Coldren Christopher D, Hall Rob, Churchill Gary A

机构信息

The Jackson Laboratory, Bar Harbor, ME, USA.

出版信息

PLoS One. 2008;3(11):e3724. doi: 10.1371/journal.pone.0003724. Epub 2008 Nov 14.

Abstract

The quality of gene expression microarray data has improved dramatically since the first arrays were introduced in the late 1990s. However, the reproducibility of data generated at multiple laboratory sites remains a matter of concern, especially for scientists who are attempting to combine and analyze data from public repositories. We have carried out a study in which a common set of RNA samples was assayed five times in four different laboratories using Affymetrix GeneChip arrays. We observed dramatic differences in the results across laboratories and identified batch effects in array processing as one of the primary causes for these differences. When batch processing of samples is confounded with experimental factors of interest it is not possible to separate their effects, and lists of differentially expressed genes may include many artifacts. This study demonstrates the substantial impact of sample processing on microarray analysis results and underscores the need for randomization in the laboratory as a means to avoid confounding of biological factors with procedural effects.

摘要

自20世纪90年代末首次引入基因表达微阵列以来,微阵列数据的质量有了显著提高。然而,多个实验室产生的数据的可重复性仍然令人担忧,特别是对于那些试图合并和分析来自公共数据库的数据的科学家来说。我们进行了一项研究,在四个不同实验室中使用Affymetrix基因芯片阵列对一组常见的RNA样本进行了五次检测。我们观察到不同实验室的结果存在显著差异,并将阵列处理中的批次效应确定为这些差异的主要原因之一。当样本的批次处理与感兴趣的实验因素混淆时,就无法区分它们的影响,差异表达基因列表可能包含许多假象。这项研究证明了样本处理对微阵列分析结果的重大影响,并强调了在实验室中进行随机化的必要性,以此作为避免生物学因素与程序效应混淆的一种手段。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6498/2579585/91efb81ce5cc/pone.0003724.g001.jpg

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本文引用的文献

1
Capturing heterogeneity in gene expression studies by surrogate variable analysis.
PLoS Genet. 2007 Sep;3(9):1724-35. doi: 10.1371/journal.pgen.0030161. Epub 2007 Aug 1.
2
Accurate ranking of differentially expressed genes by a distribution-free shrinkage approach.
Stat Appl Genet Mol Biol. 2007;6:Article9. doi: 10.2202/1544-6115.1252. Epub 2007 Feb 23.
3
Metagene projection for cross-platform, cross-species characterization of global transcriptional states.
Proc Natl Acad Sci U S A. 2007 Apr 3;104(14):5959-64. doi: 10.1073/pnas.0701068104. Epub 2007 Mar 27.
4
Using GOstats to test gene lists for GO term association.
Bioinformatics. 2007 Jan 15;23(2):257-8. doi: 10.1093/bioinformatics/btl567. Epub 2006 Nov 10.
5
Gene expression analysis of mouse chromosome substitution strains.
Mamm Genome. 2006 Jun;17(6):598-614. doi: 10.1007/s00335-005-0176-y. Epub 2006 Jun 12.
6
Linear models and empirical bayes methods for assessing differential expression in microarray experiments.
Stat Appl Genet Mol Biol. 2004;3:Article3. doi: 10.2202/1544-6115.1027. Epub 2004 Feb 12.
7
Standardizing global gene expression analysis between laboratories and across platforms.
Nat Methods. 2005 May;2(5):351-6. doi: 10.1038/nmeth754. Epub 2005 Apr 21.
8
Multiple-laboratory comparison of microarray platforms.
Nat Methods. 2005 May;2(5):345-50. doi: 10.1038/nmeth756. Epub 2005 Apr 21.
9
Independence and reproducibility across microarray platforms.
Nat Methods. 2005 May;2(5):337-44. doi: 10.1038/nmeth757. Epub 2005 Apr 21.
10
Improved statistical tests for differential gene expression by shrinking variance components estimates.
Biostatistics. 2005 Jan;6(1):59-75. doi: 10.1093/biostatistics/kxh018.

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