Quantitative quality control in microarray image processing and data acquisition

Abstract

A new integrated image analysis package with quantitative quality control schemes is described for cDNA microarray technology. The package employs an iterative algorithm that utilizes both intensity characteristics and spatial information of the spots on a microarray image for signal-background segmentation and defines five quality scores for each spot to record irregularities in spot intensity, size and background noise levels. A composite score qcom is defined based on these individual scores to give an overall assessment of spot quality. Using qcom we demonstrate that the inherent variability in intensity ratio measurements is closely correlated with spot quality, namely spots with higher quality give less variablemeasurements and vice versa. In addition, gauging data byqcom can improve data reliability dramatically and efficiently. We further show that the variability in ratio measurements drops exponentially with increasing qcom and, for the majority of spots at the high quality end, this improvement ismainly due to an improvement in correlation between the two dyes. Based on these studies, we discuss the potential of quantitative quality control for microarray data and the possibility of filtering and normalizing microarray data using a quality metrics-dependent scheme.