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Gupta R, Arjas E, Kulathinal S, et al. Bayesian hierarchical model for estimating gene expression intensity using multiple scanned microarrays. EURASIP J Bioinform Syst Biol. 2008;231950doi: 10.1155/2008/231950.
Gupta, R., Arjas, E., Kulathinal, S., Thomas, A., & Auvinen, P. (2008). Bayesian hierarchical model for estimating gene expression intensity using multiple scanned microarrays. EURASIP journal on bioinformatics & systems biology, 231950. https://doi.org/10.1155/2008/231950
Gupta, Rashi, et al. "Bayesian hierarchical model for estimating gene expression intensity using multiple scanned microarrays." EURASIP journal on bioinformatics & systems biology vol. (2008): 231950. doi: https://doi.org/10.1155/2008/231950
Gupta R, Arjas E, Kulathinal S, Thomas A, Auvinen P. Bayesian hierarchical model for estimating gene expression intensity using multiple scanned microarrays. EURASIP J Bioinform Syst Biol. 2008;231950. doi: 10.1155/2008/231950. PMID: 18464926; PMCID: PMC3171389.
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EURASIP J Bioinform Syst Biol. 2008;231950. doi: 10.1155/2008/231950.

Bayesian hierarchical model for estimating gene expression intensity using multiple scanned microarrays.

EURASIP journal on bioinformatics & systems biology

Rashi Gupta, Elja Arjas, Sangita Kulathinal, Andrew Thomas, Petri Auvinen

Affiliations

  1. Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland. [email protected]

PMID: 18464926 PMCID: PMC3171389 DOI: 10.1155/2008/231950

Abstract

We propose a method for improving the quality of signal from DNA microarrays by using several scans at varying scanner sensitivities. A Bayesian latent intensity model is introduced for the analysis of such data. The method improves the accuracy at which expressions can be measured in all ranges and extends the dynamic range of measured gene expression at the high end. Our method is generic and can be applied to data from any organism, for imaging with any scanner that allows varying the laser power, and for extraction with any image analysis software. Results from a self-self hybridization data set illustrate an improved precision in the estimation of the expression of genes compared to what can be achieved by applying standard methods and using only a single scan.

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