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Presentation: Integrative analysis of transcriptomics and proteomics data with ArrayMining and TopoGSA
The increasing availability of large-scale biological datasets has not only led to the development of many specialized bioinformatics analysis methods but also entails the opportunity and challenge to combine already available algorithms and datasets as building blocks in new meta-level approaches.
In our web-applications for microarray and gene/protein set analysis, ArrayMining.net and TopoGSA, we present various integrative methods, including ensemble and con¬sensus techniques as well as modular combinations of different analysis types, to ex¬tract new insights from experimental data. Apart from the combination of closely re¬lated datasets and algorithms, the major purpose of these tools is to integrate knowl¬edge extraction methods from widely different fields, e.g. statistics, optimisation and topological network analysis.
As an example for these integrative analysis techniques, we present a microarray con¬sensus-clustering approach based on Simulated Annealing, which is part of the ArrayMining.net Class Discovery Analysis module, and demonstrate how this ap¬proach can be combined in a modular fashion with a prior gene set analysis. The re¬sults reveal that improved cluster validity indices can be obtained by merging the two methods, and provide pointers to distinct sub-classes within pre-defined tumour categories for a breast cancer dataset by the Nottingham Queens Medical Centre.
In the second part of the talk, we show how results from a supervised microarray fea¬ture selection analysis on ArrayMining.net can be investigated in further detail with TopoGSA, a new web-tool for network topological analysis of gene/protein sets mapped on a comprehensive human protein-protein interaction network. Finally, we discuss results from a TopoGSA analysis of the complete set of genes currently known to be mutated in cancer (Futreal et al., 2004). The presented web-applications are freely available at www.infobiotics.net and the work have been published recently (Glaab, Garibaldi and Krasnogor, 2009, BMC Bioinformatics; Glaab, Baudot, Krasnogor and Valencia, 2010, Bioinformatics).