Complex networks approach to gene expression driven phenotype imaging

摘要

Motivation: The need is to visualize and quantify gene expression spatial patterns. Because of their generality for representation of interaction among several elements, complex networksare used to measure the spatial interactions and adjacencies defined by gene expression patterns. Results: Enhanced visualization of spatial interactions between elements where genes are expressed is possible, allowing the identification of structures which would go unnoticed by using conventional imaging. The quantification of the expression intensity in terms of the node degree and clustering coefficient allows the identification of different types of interactions, yielding insights about cell signaling and differentiation, and providing the basis for comparison and discrimination of the patterns along the developmental stages. Availability: Supplementary Material, including visualizations as well as the basic routines for translating gene expression images into complex networks and obtaining node degree and clustering coefficient measurements, are provided. Availability: Supplementary Material, including visualizations as well as the basic routines for translating gene expression images into complex networks and obtaining node degree and clustering coefficient measurements, are provided.