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A gene expression bar code for microarray data

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Abstract

The ability to measure genome-wide expression holds great promise for characterizing cells and distinguishing diseased from normal tissues. Thus far, microarray technology has been useful only for measuring relative expression between two or more samples, which has handicapped its ability to classify tissue types. Here we present a method that can successfully predict tissue type based on data from a single hybridization. A preliminary web-tool is available online (http://rafalab.jhsph.edu/barcode/).

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Figure 1: Across-sample distributions of gene-expression estimates.
Figure 2: Demonstration of the lab effect and its removal by the bar-code algorithm.

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Author information

Author notes

  1. Michael J Zilliox

    Present address: Present address: Emory Vaccine Center and Department of Microbiology and Immunology, Emory University School of Medicine, 1510 Clifton Road, Atlanta, Georgia 30322, USA.,

Authors and Affiliations

  1. W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, 21205, Maryland, USA

    Michael J Zilliox

  2. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, 21205, Maryland, USA

    Rafael A Irizarry

Authors
  1. Michael J Zilliox
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  2. Rafael A Irizarry
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Corresponding author

Correspondence to Rafael A Irizarry.

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Supplementary Text and Figures

Supplementary Figures 1–5, Supplementary Tables 1–6, Supplementary Methods, Supplementary Results. (PDF 3811 kb)

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Zilliox, M., Irizarry, R. A gene expression bar code for microarray data. Nat Methods 4, 911–913 (2007). https://doi.org/10.1038/nmeth1102

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  • DOI: https://doi.org/10.1038/nmeth1102

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