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Using the QAPgrid Visualization Approach for Biomarker Identification of Cell-Specific Transcriptomic Signatures

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Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1526))

Abstract

In this chapter, we illustrate the use of an integrated mathematical method for joint clustering and visualization of large-scale datasets. In applying these clustering methodologies to biological datasets, we aim to identify differentially expressed genes according to cell type by building molecular signatures supported by statistical scores. In doing so, we also aim to find a global map of highly co-expressed clusters. Variations in these clusters may well indicate other pathological trends and changes.

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Acknowledgments

The authors would like to thank Prof. Manuel Graeber for his suggestion about the datasets to be explored.

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Authors and Affiliations

  1. Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine (CIBM), Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW, Australia

    Chloe Warren & Pablo Moscato

  2. Departamento de Ingeniería Informática, Facultad de Ingeniería, Universidad de Santiago de Chile, Santiago, Chile

    Mario Inostroza-Ponta

Authors
  1. Chloe Warren
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  2. Mario Inostroza-Ponta
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  3. Pablo Moscato
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Corresponding author

Correspondence to Pablo Moscato .

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Editors and Affiliations

  1. Monash University, Melbourne, Victoria, Australia

    Jonathan M. Keith

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Warren, C., Inostroza-Ponta, M., Moscato, P. (2017). Using the QAPgrid Visualization Approach for Biomarker Identification of Cell-Specific Transcriptomic Signatures. In: Keith, J. (eds) Bioinformatics. Methods in Molecular Biology, vol 1526. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6613-4_16

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  • DOI: https://doi.org/10.1007/978-1-4939-6613-4_16

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6611-0

  • Online ISBN: 978-1-4939-6613-4

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