A statistical procedure for flagging weak spots greatly improves normalization and ratio estimates in microarray experiments

Over the last few years, there has been a dramatic increase in the use of cDNA microarrays to monitor gene expression changes in biological systems. Data from these experiments are usually transformed into expression ratios between experimental samples and a common reference sample for subsequent data analysis. The accuracy of this critical transformation depends on two major parameters: the signal intensities and the normalization of the experiment versus reference signal intensities. Here we describe and validate a new model for microarray signal intensity that has one multiplicative variation and one additive background variation. Using replicative experiments and simulated data, we found that the signal intensity is the most critical parameter that influences the performance of normalization, accuracy of ratio estimates, reproducibility, specificity and sensitivity of microarray experiments. Therefore, we developed a statistical procedure to flag spots with weak signal intensity based on the standard deviation (δ ij) of background differences between a spot and the neighboring spots, i.e. a spot is considered as too weak if the signal is weaker than cδ ij. Our studies suggest that normalization and ratio estimates were unacceptable when this threshold (c) is small. We further showed that when a reasonable compromise of c (c = 6) is applied, normalization using trimmed mean of log ratios performed slightly better than global intensity and mean of ratios. These studies suggest that decreasing the background noise is critical to improve the quality of microarray experiments. 3 The tremendous advance of the human genome project and development of new high-throughput technologies has created unparalleled opportunities to study the mechanism of disease, monitor disease progression and evaluate effective therapies. As more and more genes are being identified, it has become extremely important to understand the function of these genes and their pathways. Global gene expression analysis is a critical component of this ambitious endeavor. Microarray technologies offer investigators an opportunity to simultaneously monitor the expression of a large number of genes in the context of their biological system. For this study, we concentrated on microarray-based studies monitoring RNA expression levels using cDNA microarrays printed on glass microscope slides. Pat Brown and coworkers developed the protocols widely used to do this type of assay (1,2,3,4,5). The basic strategy for this type of analysis is to isolate RNA from two sources, a reference and an experimental sample. The RNA samples are converted to cDNA and labeled with a fluorophore, typically Cy3 …