Multicriteria Gene Screening for Analysis of Differential Expression with DNA Microarrays
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Multicriteria Gene Screening for Analysis of Differential Expression with DNA Microarrays Alfred O. Hero Departments of Electrical Engineering and Computer Science, Biomedical Engineering, and Statistics, University of Michigan, Ann Arbor, MI 48109, USA Email: [email protected]
Gilles Fleury Service des Mesures, Ecole Sup´erieure d’Electricit´e, 91192 Gif-sur-Yvette, France Email: [email protected]
Alan J. Mears Departments of Ophthalmology and Visual Sciences, and Human Genetics, University of Michigan Medical School, Ann Arbor, MI 48109, USA University of Ottawa Eye Institute, Ottawa Health Research Institute, Ottawa, ON Canada, K1H 8L6 Email: [email protected]
Anand Swaroop Departments of Ophthalmology and Visual Sciences, and Human Genetics, University of Michigan Medical School, Ann Arbor, MI 48109, USA Email: [email protected] Received 10 May 2003; Revised 30 August 2003 This paper introduces a statistical methodology for the identification of differentially expressed genes in DNA microarray experiments based on multiple criteria. These criteria are false discovery rate (FDR), variance-normalized differential expression levels (paired t statistics), and minimum acceptable difference (MAD). The methodology also provides a set of simultaneous FDR confidence intervals on the true expression differences. The analysis can be implemented as a two-stage algorithm in which there is an initial screen that controls only FDR, which is then followed by a second screen which controls both FDR and MAD. It can also be implemented by computing and thresholding the set of FDR P values for each gene that satisfies the MAD criterion. We illustrate the procedure to identify differentially expressed genes from a wild type versus knockout comparison of microarray data. Keywords and phrases: bioinformatics, gene filtering, gene profiling multiple comparisons, familywise error rates.
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INTRODUCTION
Since Watson and Crick discovered DNA more than fifty years ago, the field of genomics has progressed from a speculative science to one of the most thriving areas of current research and development [1]. After successful completion (99%) of the Human Genome project [2], attention is turning to “functional genomics” and “proteomics,” thanks principally to remarkable advances in computations and technology. These disciplines encompass the greater challenge of understanding the complex functional behavior and interaction of genes and their encoded proteins at the cellular level. This task has been significantly aided by the advent of DNA microarray technology and associated algorithms that enable researchers to filter through daunting amounts of data and
genetic information. In this paper, we describe a new approach to extracting a subset of differentially expressed genes from DNA microarray data. A DNA microarray consists of a large number of DNA probe sequences that are put at defined positions on a solid support such as a glass slide or a silicon wafer [3, 4]. After hybridization of a fluorescently labelled sample (gene transcripts) to
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