Functional Analysis of Transcriptional Networks

"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 and 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.

FunNet: an integrative tool for exploring transcriptional interactions

Prifti E, Zucker JD, Clément K and Henegar C.

Bioinformatics 2008, doi: 10.1093/bioinformatics/btn492.

Online Full Text

We describe here an exploratory tool, called FunNet, which implements an original systems biology approach, aiming to improve the biological relevance of the modular interaction patterns identified in transcriptional co-expression networks. A suitable analytical model, involving two abstraction layers, has been devised to relate expression profiles to the knowledge on transcripts' biological roles, extracted from genomic databases, into a comprehensive exploratory framework. This approach has been implemented into a user-friendly web tool to promote its open use by the community. Availability: http://www.funnet.info.

Henegar C, Tordjman J, Achard V, Lacasa D, Cremer I, Guerre-Millo M, Poitou C, Basdevant A,

 Stich V, Viguerie N, Langin D, Bedossa P, Zucker J-D, Clement K.

Genome Biology 2008 Jan 21;9(1):R14

 Online Supplementary Data

Investigations performed in mice and humans have acknowledged obesity as a low-grade inflammatory disease. Several molecular mechanisms have been convincingly involved in activating inflammatory processes and altering cell composition in white adipose tissue (WAT). However, the overall importance of these alterations, and their long-term impact on the metabolic functions of the WAT and on its morphology, remain unclear. Here, we analyzed the transcriptomic signature of the subcutaneous WAT in obese human subjects, in stable weight conditions and after weight loss following bariatric surgery. An original integrative functional genomics approach was applied to quantify relations between relevant structural and functional themes annotating differentially expressed genes, to construct a comprehensive map of transcriptional interactions defining the obese WAT. These analyses highlighted a significant up-regulation of genes and biological themes related to extracellular matrix (ECM) constituents, including members of integrins family, and suggested that these elements could play a major mediating role in a chain of interactions which connects local inflammatory phenomena to the alteration of WAT metabolic functions in obese subjects. Tissue and cellular investigations, driven by the analysis of transcriptional interactions, revealed an increased amount of interstitial fibrosis in obese WAT, associated with an infiltration of different types of inflammatory cells, and suggested that phenotypic alterations of human preadipocytes, induced by a proinflammatory environment, may lead to an excessive synthesis of ECM components. This study opens new perspectives in understanding the biology of human WAT and its pathologic changes indicative of tissue deterioration, associated with the development of obesity.

Unsupervised multiple-instance learning for functional profiling

of genomic data

Henegar C., Clément K. and Zucker J.-D.

LNCS/LNAI: ECML 2006. Springer Berlin/Heidelberg, 4212/2006:186-197.

Multiple-instance learning (MIL) is a popular concept among the AI community to support supervised learning applications in situations where only incomplete knowledge is available. We propose an original reformulation of the MIL concept for the unsupervised context (UMIL), which can serve as a broader framework for clustering data objects adequately described by the multiple-instance representation. Three algorithmic solutions are suggested by derivation from available conventional methods: agglomerative or partition clustering and MIL’s citation-kNN approach. Based on standard clustering quality measures, we evaluated these algorithms within a bioinformatic framework to perform a functional profiling of two genomic data sets, after relating expression data to biological annotations into an UMIL representation. Our analysis spotlighted meaningful interaction patterns relating biological processes and regulatory pathways into coherent functional modules, uncovering profound features of the biological model. These results indicate UMIL’s usefulness in exploring hidden behavioral patterns from complex data.

Implementation

Last updated: 18.09.2008

© 2003 - 2008 by Corneliu Henegar

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