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Bioinformatics Advance Access published online on April 21, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti447
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received January 18, 2005
Revised March 29, 2005
Accepted April 9, 2005

Article

Donuts, scratches and blanks: robust model-based segmentation of microarray images

Qunhua Li 1, Chris Fraley 1*, Roger E. Bumgarner 2, Ka Yee Yeung 2, and Adrian E. Raftery 1

1 Department of Statistics, Box 354322, University of Washington, Seattle, WA 98195
2 Department of Microbiology, Box 357242, University of Washington, Seattle, WA 98195

* To whom correspondence should be addressed.
Chris Fraley, E-mail: fraley{at}stat.washington.edu


  Abstract

Motivation: Inner holes, artifacts and blank spots are common in microarray images, but current image analysis methods do not pay them enough attention. We propose a new robust model-based method for processing microarray images so as to estimate foreground and background intensities. The method starts with a very simple but effective automatic gridding method, and then proceeds in two steps. The first step applies model-based clustering to the distribution of pixel intensities, using the Bayesian Information Criterion (BIC) to choose the number of groups up to a maximum of three. The second step is spatial, finding the large spatially connected components in each cluster of pixels. The method thus combines the strengths of histogram-based and spatial approaches. It deals effectively with inner holes in spots and with artifacts. It also provides a formal inferential basis for deciding when the spot is blank, namely when the BIC favors one group over two or three.

Results: We apply our methods for gridding and segmentation to cDNA microarray images from an HIV infection experiment. In these experiments, our method had better stability across replicates than a fixed-circle segmentation method or the seeded region growing method in the SPOT software, without introducing noticeable bias when estimating the intensities of differentially expressed genes.

Availability: spotSegmentation, an R language package implementing both the gridding and segmentation methods is available through the Bioconductor project (http://www.bioconductor.org). The segmentation method requires the contributed R package MCLUST for model-based clustering (http://cran.us.r-prcdoject.org).


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