Analysis of Multiplex Gene Expression Maps Obtained by Voxelation

In this paper we present an approach for identifying the relationships between gene expression maps and gene functions based on the multiplex gene expression maps of mouse brain obtained by voxelation. To analyze the dataset, we choose typical genes as queries and aim at discovering similar gene groups. We use the wavelet transform for extracting features from the left and right hemispheres averaged gene expression maps, and the Euclidean distance between each pair of feature vectors to determine gene similarity. We also perform a multiple clustering approach on the gene expression maps, combined with hierarchical clustering. Among each group of similar genes and clusters, the gene function similarity is measured by calculating the average gene function distances in the gene ontology structure. The experimental results confirm the hypothesis that genes with similar gene expression maps might have similar gene functions. The voxelation data takes into account the location information of gene expression level in mouse brain, which is novel in related research. The proposed approach can potentially be used to predict gene functions and provide helpful suggestions to biologists.