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.

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