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Bioinformatics Advance Access published online on September 28, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti051
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received April 23, 2004
Revised September 17, 2004
Accepted September 20, 2004

Article

Weighted analysis of microarray gene expression using maximum likelihood

David J. Bakewell 1* and Ernst Wit 2

1 Beatson Laboratories CR UK, Garscube Estate, Bearsden G61 1BD, UK
2 Department of Statistics, University of Glasgow, Glasgow G12 8QW, UK

* To whom correspondence should be addressed. E-mail: d.bakewell{at}beatson.gla.ac.uk.


  Abstract

Motivation: The numerical values of gene expression measured using microarrays are usually presented to the biological end-user as summary statistics of spot pixel data, such as the spot mean, median and mode. Much of the subsequent data analysis reported in the literature, however, uses only one of these spot statistics. This results in sub-optimal estimates of gene expression levels and a need for improvement in quantitative spot variation surveillance.

Results: This paper develops a maximum likelihood method for estimating gene expression using spot mean, variance, and pixel number values available from typical microarray scanners. It employs a hierarchical model of variation between and within microarray spots. The hierarchical Maximum Likelihood Estimate (MLE) is shown to be a more efficient estimator of the mean than the ‘conventional’ estimate using solely the spot mean values (i.e. without spot variance data). Furthermore, under the assumptions of our model, the spot mean and spot variance are shown to be sufficient statistics which do not require the use of all pixel data.

The hierarchical MLE method is applied to data from both Monte Carlo (MC) simulations and a two-channel dye-swapped spotted microarray experiment. The MC simulations show the hierarchical MLE method leads to improved detection of differential gene expression particularly when ‘outlier’ spots are present on the arrays. Compared with the conventional method, the MLE method applied to data from the microarray experiment leads to an increase in the number of differentially expressed genes detected for low cut-off p-values of interest.

Availability: The Matlab code is available at http://www.stats.gla.ac.uk/~microarray/software/.


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