FunNet: Integrative Functional Analysis of Transcriptional Networks
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.