Reducing the variability in cDNA microarray image processing by Bayesian inference.

Neil D Lawrence; Marta Milo; Mahesan Niranjan; Penny Rashbass; Stephan Soullier

doi:10.1093/bioinformatics/btg438

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Reducing the variability in cDNA microarray image processing by Bayesian inference.

Neil D Lawrence·Marta Milo·Mahesan Niranjan·Penny Rashbass·Stephan Soullier

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Abstract

MOTIVATION: Gene expression levels are obtained from microarray experiments through the extraction of pixel intensities from a scanned image of the slide. It is widely acknowledged that variabilities can occur in expression levels extracted from the same images by different users with the same software packages. These inconsistencies arise due to differences in the refinement of the placement of the microarray 'grids'. We introduce a novel automated approach to the refinement of grid placements that is based upon the use of Bayesian inference for determining the size, shape and positioning of the microarray 'spots', capturing uncertainty that can be passed to downstream analysis. RESULTS: Our experiments demonstrate that variability between users can be significantly reduced using the approach. The automated nature of the approach also saves hours of researchers' time normally spent in refining the grid placement.

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Algorithms Artifacts Bayes Theorem Gene Expression Profiling Genetic Variation Image Enhancement Image Interpretation, Computer-Assisted Microscopy, Fluorescence Models, Genetic Models, Statistical Observer Variation Oligonucleotide Array Sequence Analysis Reproducibility of Results Sensitivity and Specificity Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't Validation Study

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Neil D Lawrence, Marta Milo, Mahesan Niranjan, Penny Rashbass, Stephan Soullier. 2004-01-22. Reducing the variability in cDNA microarray image processing by Bayesian inference.. https://doi.org/10.1093/bioinformatics%2Fbtg438

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