Complex networks approach to gene expression driven phenotype imaging

Motivation: 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: Enhanc...

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Bibliographic Details
Published inBioinformatics Vol. 21; no. 20; pp. 3846 - 3851
Main Authors Diambra, L., Costa, L. da F.
Format Journal Article
LanguageEnglish
Published Oxford Oxford University Press 15.10.2005
Oxford Publishing Limited (England)
Subjects
Algorithms
Biological and medical sciences
Cluster Analysis
Computer Graphics
Fundamental and applied biological sciences. Psychology
Gene Expression Profiling - methods
Gene Expression Regulation - physiology
General aspects
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Genetic
Oligonucleotide Array Sequence Analysis - methods
Phenotype
Signal Transduction - physiology
Software
User-Computer Interface
Visualization
Transformation
Insecta
Drosophila
Interaction
Representation
Review
Gene expression
Drosophilidae
Clusterin
Phenotype
Gene
Arthropoda
Network
Invertebrata
Structure
Bioinformatics
Cell
Diptera
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Summary:Motivation: 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. 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. Contact: luciano@if.sc.usp.br; diambra@univap.br
Bibliography:To whom correspondence should be addressed.
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ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/bti625