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Pixel-level Signal Modelling with Spatial Correlation for Two-Colour Microarrays

Author

Listed:
  • Ekstrøm Claus T

    (Dept. Natural Sciences, Royal Veterinary and Agricultural University)

  • Bak Søren

    (Dept. of Plant Biology and Center of Molecular Plant Physiology (PlaCe), Royal Veterinary and Agricultural University)

  • Rudemo Mats

    (Dept. Natural Sciences, Royal Veterinary and Agricultural University)

Abstract

Statistical models for spot shapes and signal intensities are used in image analysis of laser scans of microarrays. Most models have essentially been based on the assumption of independent pixel intensity values, but models that allow for spatial correlation among neighbouring pixels can accommodate errors in the microarray slide and should improve the model fit. Five spatial correlation structures, exponential, Gaussian, linear, rational quadratic and spherical, are compared for a dataset with 50-mer two-colour oligonucleotide microarrays and 452 probes for selected Arabidopsis genes. Substantial improvement in model fit is obtained for all five correlation structures compared to the model with independent pixel values, and the Gaussian and the spherical models seem to be slightly better than the other three models. We also conclude that for the data set analysed the correlation seems negligible for non-neighbouring pixels.

Suggested Citation

  • Ekstrøm Claus T & Bak Søren & Rudemo Mats, 2005. "Pixel-level Signal Modelling with Spatial Correlation for Two-Colour Microarrays," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-16, April.
    • Handle: RePEc:bpj:sagmbi:v:4:y:2005:i:1:n:6
    DOI: 10.2202/1544-6115.1112
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    Cited by:

    1. Chan, Shih-Huang & Chang, Wan-Chi, 2009. "A robust ratio estimator of gene expression via inverse-variance weighting for cDNA microarray images," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1577-1589, March.
    2. Arteaga-Salas Jose Manuel & Harrison Andrew P & Upton Graham J. G., 2008. "Reducing Spatial Flaws in Oligonucleotide Arrays by Using Neighborhood Information," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-19, October.

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