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Bioinformatics Advance Access published online on November 22, 2005
Bioinformatics, 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@oxfordjournals.org
Received August 9, 2005
Revised November 10, 2005
Accepted November 15, 2005

Article

Statistical estimation of gene expression using multiple laser scans of microarrays

Mizanur R. Khondoker 1 *, Chris A. Glasbey 2, and Bruce J. Worton 3

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

* To whom correspondence should be addressed.
Mizanur R. Khondoker, E-mail: mizanur{at}bioss.ac.uk


  Abstract

Summary: We propose a statistical model for estimating gene expression using data from multiple laser scans at different settings of hybridised microarrays. A functional regression model is used, based on a nonlinear 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 65535, 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 codes for implementing the method can be obtained from the authors.


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