Zhou, D., Liu, R. and Xiong, S. 2014. Analysis and integration of microarray data of Arabidopsis mutants. Can. J. Plant Sci. 94: 235-243. Nowadays, high-throughput microarray data make it possible to study biological data on a large scale. It has successfully been applied to the gene function prediction in yeast, hypersensitive response in response to pathogen and human cancer. However, within the microarray data, there exists lots of unknown information which is worth mining. Based on mutants' signature genes of Arabidopsis thaliana, we constructed a reference matrix including 267 pairs of subsets of differential reference profiles. We analyzed our data through expression profiles and connectivity map. Two notable results were detected by comparing every mutant in the matrix. Above all, the data mining procedure confirmed the biological relations not only between different stresses and glucose metabolism, but also stresses and MAPK signaling pathway among HSP90, PGM, VTE1, AXR4, SFR6, and SFR2 mutants. In addition, sfr6 might be involved in light cycle regulations, in accordance with the results of the overlap analysis.