Functional Profiling of cDNA Microarray Expression Data
FunCluster performs a functional analysis of microarray data based
Gene Ontology & KEGG functional annotations. Based on expression
data and functional annotations FunCluster builds classes of
putatively co-regulated biological processes through a specially
designed clustering procedure involving functional annotations
and expression data.

FunCluster performs a functional analysis of microarray expression
data based on Gene Ontology & KEGG functional annotations. From
expression data and functional annotations FunCluster builds classes
of putatively co-regulated biological processes through a specially
designed clustering procedure.

Functional Profiling of Microarray Expression Data

FunCluster performs a functional analysis of microarray
expression data based on Gene Ontology & KEGG functional
annotations. From expression data and functional annotations
FunCluster builds classes of putatively co-regulated biological
processes through a specially designed clustering procedure.

Functional Analysis of Gene Co-expression Networks
R (>= 2.4.0), ade4, cluster, Hmisc, nlme, sna, Cairo
FunNet is an integrative tool for analyzing gene co-expression networks 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.