A General Framework for

Weighted Gene Co-expression Network Analysis

Technical Report & R Software Tutorial

Bin Zhang and Steve Horvath

Correspondence: shorvath@mednet.ucla.edu

http://www.biostat.ucla.edu/people/horvath.htm

Human Genetics and Department of Biostatistics

University of California, Los Angeles


Abstract

Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for `soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion).


Acknowledgement


Content

1.      Journal Article (Statistical Applications in Genetics and Molecular Biology)

2.    R tutorial

A.      Brain Cancer Gene Co-expression Network Analysis

           Long GBM Tutorial

           Short GBM Tutorial

           PDF version

           Custom made network R functions

           Brain Microarray Data (Courtesy of Stan Nelson, UCLA microarray core)

                Comma delimited Microarray data

B.      YEAST Gene Co-expression Network Analysis

           Microsoft word version (recommended)

           PDF version

           Custom made network R functions

           Yeast Microarray Data

                Comma delimited Microarray data

C.      Simulated Example

        For more comprehensive simulation studies, please visit the simulation studies page.

3.   Talk on the use of weighted co-expression networks
PowerPoint version

PDF version

4.    Wiki Dictionary of terms and reading lists

To cite the technical report, please use: Bin Zhang and Steve Horvath (2005) "A General Framework for Weighted Gene Co-Expression Network Analysis", Statistical Applications in Genetics and Molecular Biology: Vol. 4: No. 1, Article 17. http://www.bepress.com/sagmb/vol4/iss1/art17

 

Other material regarding weighted gene co-expression network analysis

             Weighted Gene Co-Expression Network Page

 


2007-07-09

Please send your suggestions and comments to: shorvath@mednet.ucla.edu