12 July 2016 Proteomic data analysis of glioma cancer stem-cell lines based on novel nonlinear dimensional data reduction techniques
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Abstract
Glioma-derived cancer stem cells (GSCs) are tumor-initiating cells and may be refractory to radiation and chemotherapy and thus have important implications for tumor biology and therapeutics. The analysis and interpretation of large proteomic data sets requires the development of new data mining and visualization approaches. Traditional techniques are insufficient to interpret and visualize these resulting experimental data. The emphasis of this paper lies in the application of novel approaches for the visualization, clustering and projection representation to unveil hidden data structures relevant for the accurate interpretation of biological experiments. These qualitative and quantitative methods are applied to the proteomic analysis of data sets derived from the GSCs. The achieved clustering and visualization results provide a more detailed insight into the protein-level fold changes and putative upstream regulators for the GSCs. However the extracted molecular information is insufficient in classifying GSCs and paving the pathway to an improved therapeutics of the heterogeneous glioma.
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Sylvain Lespinats, Sylvain Lespinats, Katja Pinker-Domenig, Katja Pinker-Domenig, Georg Wengert, Georg Wengert, Ivo Houben, Ivo Houben, Marc Lobbes, Marc Lobbes, Andreas Stadlbauer, Andreas Stadlbauer, Anke Meyer-Bäse, Anke Meyer-Bäse, } "Proteomic data analysis of glioma cancer stem-cell lines based on novel nonlinear dimensional data reduction techniques", Proc. SPIE 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016, 98710T (12 July 2016); doi: 10.1117/12.2229133; https://doi.org/10.1117/12.2229133
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