A hill-climbing approach for automatic gridding of cDNA microarray images

Image and statistical analysis are two important stages of cDNA microarrays. Of these, gridding is necessary to accurately identify the location of each spot while extracting spot intensities from the microarray images and automating this procedure permits high-throughput analysis. Due to the deficiencies of the equipment used to print the arrays, rotations, misalignments, high contamination with noise and artifacts, and the enormous amount of data generated, solving the gridding problem by means of an automatic system is not trivial. Existing techniques to solve the automatic grid segmentation problem cover only limited aspects of this challenging problem and require the user to specify the size of the spots, the number of rows and columns in the grid, and boundary conditions. In this paper, a hill-climbing automatic gridding and spot quantification technique is proposed which takes a microarray image (or a subgrid) as input and makes no assumptions about the size of the spots, rows, and columns in the grid. The proposed method is based on a hill-climbing approach that utilizes different objective functions. The method has been found to effectively detect the grids on microarray images drawn from databases from GEO and the Stanford genomic laboratories.