Image analysis for detecting faulty spots from microarray images

Abstract

Microarrays allow the monitoring of thousands of genes simultaneously. Before a measure of gene activity of an organism is obtained, however, many stages in the error-prone manual and automated process have to be performed. Without quality control, the resulting measures may, instead of being estimates of gene activity, be due to noise or systematic variation. We address the problem of detecting spots of low quality from the microarray images to prevent them to enter the subsequent analysis. We extract features describing spatial characteristics of the spots on the microarray image and train a classifier using a set of labeled spots. We assess the results for classification of individual spots using ROC analysis and for a compound classification using a non-symmetric cost structure for misclassifications.