Analysis of variance for gene expression microarray data

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Analysis of variance for gene expression microarray data (2000)

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by M. Kathleen Kerr , Mitchell Martin , Gary A. Churchill

Venue:	Journal of Computational Biology
Citations:	360 - 5 self

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@ARTICLE{Kerr00analysisof,
    author = {M. Kathleen Kerr and Mitchell Martin and Gary A. Churchill},
    title = {Analysis of variance for gene expression microarray data},
    journal = {Journal of Computational Biology},
    year = {2000},
    pages = {819--837}
}

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Abstract

Spotted cDNA microarrays are emerging as a powerful and cost-effective tool for largescale analysis of gene expression. Microarrays can be used to measure the relative quantities of speci � c mRNAs in two or more tissue samples for thousands of genes simultaneously. While the power of this technology has been recognized, many open questions remain about appropriate analysis of microarray data. One question is how to make valid estimates of the relative expression for genes that are not biased by ancillary sources of variation. Recognizing that there is inherent “noise ” in microarray data, how does one estimate the error variation associated with an estimated change in expression, i.e., how does one construct the error bars? We demonstrate that ANOVA methods can be used to normalize microarray data and provide estimates of changes in gene expression that are corrected for potential confounding effects. This approach establishes a framework for the general analysis and interpretation of microarray data. Key words: Gene expression microarray, differential expression, analysis of variance, bootstrap.

Keyphrases

microarray data gene expression microarray data gene expression ancillary source cdna microarrays relative expression error variation error bar valid estimate many open question potential confounding effect tissue sample anova method relative quantity appropriate analysis cost-effective tool inherent noise gene expression microarray general analysis key word estimated change largescale analysis differential expression

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