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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

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

Claus T Ekstrøm1 / Søren Bak2 / Mats Rudemo3

1Dept. Natural Sciences, Royal Veterinary and Agricultural University

2Dept. of Plant Biology and Center of Molecular Plant Physiology (PlaCe), Royal Veterinary and Agricultural University

3Dept. Natural Sciences, Royal Veterinary and Agricultural University

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 4, Issue 1, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.2202/1544-6115.1112, April 2005

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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.

Keywords: spotted array; spatial correlation; censored data; polynomial-hyperbolic model

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