FunNet is an integrative tool for analyzing gene co-expression networks built from microarray expression data. The analytic model implemented in this library involves two abstraction layers: transcriptional and functional (biological roles). A functional profiling technique using Gene Ontology & KEGG annotations is applied to extract a list of relevant biological themes from microarray expression profiling data. Afterwards multiple-instance representations are built to relate significant themes to their transcriptional instances (i.e. the two layers of the model). An adapted non-linear dynamical system model is used to quantify the proximity of relevant genomic themes based on the similarity of the expression profiles of their gene instances. Eventually an unsupervised multiple-instance clustering procedure, relying on the two abstraction layers, is used to identify the structure of the co-expression network composed from modules of functionally related transcripts. Functional and transcriptional maps of the co-expression network are provided separately together with detailed information on the network centrality of related transcripts and genomic themes.
| Version: | 1.00-5 |
| Depends: | R (≥ 2.6.0), ade4, cluster, Hmisc, nlme, sna, Cairo |
| Date: | 2008-11-11 |
| Author: | Corneliu Henegar |
| Maintainer: | Corneliu Henegar <corneliu at henegar.info> |
| License: | GPL (≥ 2) |
| URL: | http://corneliu.henegar.info/FunNet.htm, http://www.geneontology.org/GO.tools.microarray.shtml#funnet, http://www.funnet.info, http://www.funnet.ws |
| CRAN checks: | FunNet results |
Downloads:
| Package source: | FunNet_1.00-5.tar.gz |
| MacOS X binary: | FunNet_1.00-5.tgz |
| Windows binary: | FunNet_1.00-5.zip |
| Reference manual: | FunNet.pdf |
| Old sources: | FunNet archive |