Robust Automatic Microarray Image Analysis

Parallel expression analysis of many genes by microarray hybridisation is one of the most promising techniques in functional genomics. The method has been successfully applied many times in medical and biological research. Our work is about automatic methods for the first stages of a microarray data analysis pipeline. Expression analysis by microarray hybridisation is a high throughput technique. While interactive, semi-automatic software is still frequently used for the analysis of scanned array images, it is highly desirable to have automatic procedures which yield better repeatability and constant quality of the expression data for later cluster analyses. Automatic methods must handle noise and the frequently occurring contaminations on microarrays. In large scale microarray experiments, automatic image analysis can save substantial amounts of work. We describe robust image processing methods that find the printed grids of spots in the scanned microarray images without the requirement of special guide spots or specially calibrated equipment. Processing of many slides from the same print batch helps to minimize the need for human intervention. We derive our method of spot intensity ratio computation from the biochemical model of differential gene expression experiments and finally discuss how different ratio computation methods can be compared. We compare results of our method to results of manual analyses using the well-known Scanalyze (M. Eisen, LBNL Berkeley) as well as recently published methods (Brown et al. (2001), PNAS 92, 8944-8949). Our automatic method yields comparable or even more accurate results than standard methods under poor hybridisation and scan quality conditions.