Supplementary Materials for the Article:

Understanding Network Concepts in Modules

 

Jun Dong and Steve Horvath*
Department of Human Genetics and Department of Biostatistics, University of California, Los Angeles, CA 90095, USA
Email: Jun Dong - jundong@ucla.edu; Steve Horvath* - shorvath@mednet.ucla.edu;
*Corresponding author

Citation

Dong J, Horvath S (2007) Understanding Network Concepts in Modules, BMC Systems Biology 2007, 1:24

Link to paper: BMC Systems Biology

Dong and Horvath (2007) was the number 1 most highly viewed article of BMC Systems Biology in 2007. See archive.
It was also the number 4 most highly viewed article of all times (since the creation of the journal, as of 05/25/20008).

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Presentation

Link to talks:    PowerPoint Version    PDF version

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Abstract

Background:

Network concepts are increasingly used in biology and genetics. For example, the clustering coefficient has been used to understand network architecture; the connectivity (also known as degree) has been used to screen for cancer targets; and the topological overlap matrix has been used to define modules and to annotate genes. Dozens of potentially useful network concepts are known from graph theory.

Results:

Here we study network concepts in special types of networks, which we refer to as approximately factorizable networks. In these networks, the pairwise connection strength (adjacency) between 2 network nodes can be factored into node specific contributions, named node `conformity'. The node conformity turns out to be highly related to the connectivity. To provide a formalism for relating network concepts to each other, we define three types of network concepts: fundamental-, conformity-based-, and approximate conformity-based concepts. Fundamental concepts include the standard definitions of connectivity, density, centralization, heterogeneity, clustering coefficient, and topological overlap. The approximate conformity-based analogs of fundamental network concepts have several theoretical advantages. First, they allow one to derive simple relationships between seemingly disparate networks concepts. For example, we derive simple relationships between the clustering coefficient, the heterogeneity, the density, the centralization, and the topological overlap. The second advantage of approximate conformity-based network concepts is that they allow one to show that fundamental network concepts can be approximated by simple functions of the connectivity in
module networks.

Conclusions:

Using protein-protein interaction, gene co-expression, and simulated data, we show that a) many networks comprised of module nodes are approximately factorizable and b) in these types of networks, simple relationships exist between seemingly disparate network concepts. Our results are implemented in freely available R software code, which can be downloaded from this webpage.

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Additional Files

  1. Complete List of Network Concepts in the Modules (Download)

  2. Network Concepts and Module Size (Download)

  3. Functional Enrichment Analysis (Gene Ontology) of the Drosophila PPI Modules (Download)

  4. Functional Enrichment Analysis (Gene Ontology) of the Yeast PPI Modules (Download)

  5. Functional Enrichment Analysis (Gene Ontology) of the Yeast Gene Co-expression Modules (Download)

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R Tutorials

For each application, please save all files to the same folder.

Drosophila Protein-Protein Interaction (PPI) Networks

R Tutorials:

Microsoft word version (recommended)

PDF version

Data Files: PPI network and node names

Network    NodeNames

Custom made network R functions (Please download both!)

file 1    file 2

Yeast Protein-Protein Interaction (PPI) Networks

R Tutorials:

Microsoft word version (recommended)

PDF version

Data Files: PPI network and node names

Network    NodeNames

Custom made network R functions (Please download both!)

file 1    file 2

Yeast Gene Co-Expression Networks

R Tutorials:

Microsoft word version (recommended)

PDF version

Data Files: Yeast Microarray Data

Comma delimited Microarray data

Custom made network R functions (Please download both!)

file 1    file 2

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Acknowledgement

We would like to acknowledge the grant support from Program Project Grant 1U19AI063603-01 and NINDS/NIMH 1U24NS043562-01. We are grateful for discussions with Andy Yip, Lora Bagryanova, Dan Geschwind, Johanna Hardin, Ken Lange, Peter Langfelder, Ai Li, Jake Lusis, Paul Mischel, Stan Nelson, Nan Zhang and Wei Zhao.

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Other Materials Regarding Weighted Gene Co-expression Network Analysis

Weighted Gene Co-Expression Network Page

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2008-05-25

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