Abstract

Motivation: Spot segmentation, the process of extracting spot foreground from the surrounding background, has a central role in microarray image analysis. Although numerous algorithms have been developed for the segmentation, extensive comparisons between the algorithms have acquired far less attention. In this study, we evaluate the performance of nine microarray segmentation algorithms. Using both simulated and real microarray experiments, we overcome the challenges in performance evaluation, arising from the lack of decent ground-truth information. The usage of simulated experiments allows us to analyze the segmentation accuracy on a single pixel level as is commonly done in traditional image processing studies. With real experiments, we indirectly measure the segmentation performance, identify significant differences between the algorithms, and study the characteristics of the resulting gene expression data.
Results: Overall, our results show clear differences between the algorithms. The results demonstrate how the segmentation performance depends on the image quality, which algorithms operate on significantly different performance levels, and how the selection of a segmentation algorithm affects the identifying of differentially expressed genes.

Supplementary figures

The following figures present supplementary results for the manuscript. For more detailed figures, click on the images.

Supplementary data

Images from both real and simulated microarray experiments are available for download.

• Real images (~162Mb)
• Simulated images (~285Mb)