Visual exploratory analysis of integrated chromosome 19 proteomic data derived from glioma cancer stem-cell lines based on novel nonlinear dimensional data reduction techniques

Sylvain Lespinats; Katja Pinker-Domenig; Uwe Meyer-Bäse; Anke Meyer-Bäse

doi:10.1117/12.2181814

20 May 2015 Visual exploratory analysis of integrated chromosome 19 proteomic data derived from glioma cancer stem-cell lines based on novel nonlinear dimensional data reduction techniques

Sylvain Lespinats, Katja Pinker-Domenig, Uwe Meyer-Bäse, Anke Meyer-Bäse

Author Affiliations +

Proceedings Volume 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII; 949607 (2015) https://doi.org/10.1117/12.2181814
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

Abstract

Chromosome 19 is known to be linked to neurodegeneration and many cancers. 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 presentation 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 expression patterns for chromosome 19 proteins.

Citation Download Citation

Sylvain Lespinats, Katja Pinker-Domenig, Uwe Meyer-Bäse, and Anke Meyer-Bäse "Visual exploratory analysis of integrated chromosome 19 proteomic data derived from glioma cancer stem-cell lines based on novel nonlinear dimensional data reduction techniques", Proc. SPIE 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII, 949607 (20 May 2015); https://doi.org/10.1117/12.2181814

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