title: Study of cerebral gene expression densities using Voronoi analysis.
abstract: As the available public cerebral gene expression image data increasingly grows, the demand for automated methods to analyze such large amount of data also increases. An important study that can be carried out on these data is related to the spatial relationship between gene expressions. Similar spatial density distribution of expression between genes may indicate they are functionally correlated, thus the identification of these similarities is useful in suggesting directions of investigation to discover gene interactions and their correlated functions. In this paper, we describe the use of a high-throughput methodology based on Voronoi diagrams to automatically analyze and search for possible local spatial density relationships between gene expression images. We tested this method using mouse brain section images from the Allen Mouse Brain Atlas public database. This methodology provided measurements able to characterize the similarity of the density distribution between gene expressions and allowed the visualization of the results through networks and Principal Component Analysis (PCA). These visualizations are useful to analyze the similarity level between gene expression patterns, as well as to compare connection patterns between region networks. Some genes were found to have the same type of function and to be near each other in the PCA visualizations. These results suggest cerebral density correlations between gene expressions that could be further explored.
authors.affiliation: Institute of Physics at São Carlos, University of São Paulo, São Carlos, SP, Brazil. mauro@ursa.ifsc.usp.br
authors.forename: Mauro
authors.initials: M
authors.lastname: Miazaki
authors.forename: Luciano da F
authors.initials: Lda F
authors.lastname: Costa
mesh_terms: D000818:Animals; D002540:Cerebral Cortex / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D057166:High-Throughput Screening Assays; D051379:Mice; D025341:Principal Component Analysis