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
Microsoft word version (recommended)
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)
Custom made network R functions
Yeast Microarray Data
Comma delimited Microarray data
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