Complex networks approach to gene expression driven phenotype imaging.
BACKGROUND
The need is to visualize and quantify gene expression spatial patterns. Because of their generality for representation of interaction among several elements, complex networks are 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.


BACKGROUND
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.


BACKGROUND
luciano@if.sc.usp.br; diambra@univap.br.
Diambra
L
L
Institute of Physics at São Carlos, University of São Paulo, Brazil. diambra@univap.br
da F Costa
L
L
D000465:Algorithms; D016000:Cluster Analysis; D003196:Computer Graphics; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010641:Phenotype; D015398:Signal Transduction / Q000502:physiology*; D012984:Software; D014584:User-Computer Interface