Skip to main content
Article thumbnail
Location of Repository

Geometric Interpretation of Gene Coexpression Network Analysis

By Steve Horvath and Jun Dong

Abstract

The merging of network theory and microarray data analysis techniques has spawned a new field: gene coexpression network analysis. While network methods are increasingly used in biology, the network vocabulary of computational biologists tends to be far more limited than that of, say, social network theorists. Here we review and propose several potentially useful network concepts. We take advantage of the relationship between network theory and the field of microarray data analysis to clarify the meaning of and the relationship among network concepts in gene coexpression networks. Network theory offers a wealth of intuitive concepts for describing the pairwise relationships among genes, which are depicted in cluster trees and heat maps. Conversely, microarray data analysis techniques (singular value decomposition, tests of differential expression) can also be used to address difficult problems in network theory. We describe conditions when a close relationship exists between network analysis and microarray data analysis techniques, and provide a rough dictionary for translating between the two fields. Using the angular interpretation of correlations, we provide a geometric interpretation of network theoretic concepts and derive unexpected relationships among them. We use the singular value decomposition of module expression data to characterize approximately factorizable gene coexpression networks, i.e., adjacency matrices that factor into node specific contributions. High and low level views of coexpression networks allow us to study the relationships among modules and among module genes, respectively. We characterize coexpression networks where hub genes are significant with respect to a microarray sample trait and show that the network concept of intramodular connectivity can be interpreted as a fuzzy measure of module membership. We illustrate our results using human, mouse, and yeast microarray gene expression data. The unification of coexpression network methods with traditional data mining methods can inform the application and development of systems biologic methods

Topics: Research Article
Publisher: Public Library of Science
OAI identifier: oai:pubmedcentral.nih.gov:2446438
Provided by: PubMed Central
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://www.pubmedcentral.nih.g... (external link)
  • Suggested articles

    Citations

    1. (2004). A
    2. (2005). A general framework for weighted gene coexpression network analysis.
    3. (2007). A primer on learning in Bayesian networks for computational biology.
    4. (2002). A simple model of global cascades on random networks.
    5. (2006). Analysis of oncogenic signaling networks in glioblastoma identifies aspm as a novel molecular target.
    6. (2002). Automated modelling of signal transduction networks.
    7. (2002). Barabasi A
    8. (1978). Centrality in social networks: conceptual clarification.
    9. (1998). Cluster analysis and display of genome-wide expression patterns.
    10. (1998). Collective dynamics of ‘small-world’ networks.
    11. (1998). Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.
    12. (2006). Computational inference of neural information flow networks.
    13. (2006). Conservation and evolution of gene coexpression networks in human and chimpanzee brains.
    14. (2007). Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut library for R.
    15. (2004). Defining transcription modules using large-scale gene expression data.
    16. (2002). Diagnosis of multiple cancer types by shrunken centroids of gene expression.
    17. (2005). Differential network expression during drug and stress response.
    18. (2000). Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks.
    19. (2007). Eigengene networks for studying the relationships between co-expression modules.
    20. (2006). Eigengene-based linear discriminant model for tumor classification using gene expression microarray data.
    21. (2000). Error and attack tolerance of complex networks.
    22. (2004). Evidence for dynamically organized modularity in the yeast protein–protein interaction network.
    23. (2006). Extracting gene networks for low-dose radiation using graph theoretical algorithms.
    24. (2000). Fundamental patterns underlying gene expression profiles: simplicity from complexity.
    25. (2006). Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks.
    26. (2007). Gene network interconnectedness and the generalized topological overlap measure.
    27. (2000). Genetic network inference: from coexpression clustering to reverse engineering.
    28. (2004). Global organization of metabolic fluxes in the bacterium Escherichia coli.
    29. (2004). Godzik A
    30. (2002). Hierarchical organization of modularity in metabolic networks.
    31. (2006). Identification of inflammatory gene modules based on variations of human endothelial cell responses to oxidized lipids.
    32. (2006). Integrating genetics and network analysis to characterize genes related to mouse weight.
    33. (2006). Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.
    34. (2001). Lethality and centrality in protein networks.
    35. (2007). Metagene projection for cross-platform, cross-species characterization of global transcriptional states.
    36. (2007). Modeling systems-level regulation of host immune responses.
    37. (2005). Modular analysis of the transcriptional regulatory network of E.
    38. (2004). Network biology: understanding the cell’s functional organization.
    39. (2003). Network component analysis: reconstruction of regulatory signals in biological systems.
    40. (2002). Network motifs: simple building blocks of complex networks.
    41. (2007). Network neighborhood analysis with the multi-node topological overlap measure.
    42. (2007). Network-based classification of breast cancer metastasis.
    43. (1915). On the ‘probable error’ of a coefficient of correlation deduced from a small sample.
    44. (2007). Orthologous transcription factors in bacteria have different functions and regulate different genes.
    45. (2001). Predicting the clinical status of human breast cancer by using gene expression profiles.
    46. (2002). Reverse engineering gene networks using singular value decomposition and robust regression.
    47. (2005). Reverse engineering the gap gene network of Drosophila melanogaster.
    48. (2005). Scale-free networks in cell biology.
    49. (2002). Siegel A
    50. (2004). Similarities and differences in genomewide expression data of six organisms.
    51. (2000). Singular value decomposition for genomewide expression data processing and modelling.
    52. (2004). Sparse graphical models for exploring gene expression data.
    53. (2007). Systematic discovery of functional modules and context-specific functional annotation of human genome.
    54. (1981). The degree variance: an index of graph heterogeneity.
    55. (2005). Topological units of environmental signal processing in the transcriptional regulatory network of Escherichia coli.
    56. (2006). Transcriptional coordination of the metabolic network in arabidopsis.
    57. (2002). Transitivefunctional annotation by shortestpath analysis of gene expression data.
    58. (2007). Understanding network concepts in modules.
    59. (2007). Weighted gene coexpression network analysis strategies applied to mouse weight.
    60. (2004). Zeng A

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.