Reducing the variability in cDNA microarray image processing by Bayesian inference

• DOI: 10.1093/bioinformatics/btg438
• Publication Date: 2004-02-27T17:23:07Z
• Authors: Neil D. Lawrence; Marta Milo; Mahesan Niranjan; Penny Rashbass; Stephan Soullier
• Keywords: Observer Variation; 0301 basic medicine; Models, Statistical; Models, Genetic; Gene Expression Profiling; Genetic Variation; Reproducibility of Results; Bayes Theorem; Image Enhancement; Sensitivity and Specificity; 03 medical and health sciences; Microscopy, Fluorescence; Image Interpretation, Computer-Assisted; Artifacts; Algorithms; Oligonucleotide Array Sequence Analysis

Abstract Motivation: Gene expression levels are obtained from microarray experiments through the extraction of pixel intensities a scanned image slide. It is widely acknowledged that variabilities can occur in extracted same images by different users with software packages. These inconsistencies arise due to differences refinement placement ‘grids’. We introduce novel automated approach grid placements based upon use Bayesian inference for determining size, shape and positioning ‘spots’, capturing uncertainty be passed downstream analysis. Results: Our demonstrate variability between significantly reduced using approach. The nature also saves hours researchers’ time normally spent refining placement. Availability: A MATLAB implementation algorithm tiff slides used our experiments, as well code necessary recreate them available non-commercial http://www.dcs.shef.ac.uk/~neil/VIS