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Bioinformatics Advance Access originally published online on November 22, 2005
Bioinformatics 2006 22(2):215-219; doi:10.1093/bioinformatics/bti790
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org

Statistical estimation of gene expression using multiple laser scans of microarrays

Mizanur R. Khondoker 1,2,*, Chris A. Glasbey 1 and Bruce J. Worton 2

1Biomathematics & Statistics Scotland, University of Edinburgh King's Buildings, Edinburgh, EH9 3JZ, Scotland, UK
2School of Mathematics, University of Edinburgh King's Buildings, Edinburgh, EH9 3JZ, Scotland, UK

*To whom correspondence should be addressed.

Summary: We propose a statistical model for estimating gene expression using data from multiple laser scans at different settings of hybridized microarrays. A functional regression model is used, based on a non-linear relationship with both additive and multiplicative error terms. The function is derived as the expected value of a pixel, given that values are censored at 65 535, the maximum detectable intensity for double precision scanning software. Maximum likelihood estimation based on a Cauchy distribution is used to fit the model, which is able to estimate gene expressions taking account of outliers and the systematic bias caused by signal censoring of highly expressed genes. We have applied the method to experimental data. Simulation studies suggest that the model can estimate the true gene expression with negligible bias.

Availability: FORTRAN 90 code for implementing the method can be obtained from the authors.

Contact: mizanur{at}bioss.ac.uk

Received on August 9, 2005; revised on November 10, 2005; accepted on November 15, 2005

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