title: Using the QAPgrid Visualization Approach for Biomarker Identification of Cell-Specific Transcriptomic Signatures.
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.
authors.affiliation: Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine (CIBM), Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW, Australia.
authors.forename: Chloe
authors.initials: C
authors.lastname: Warren
authors.affiliation: Departamento de Ingeniería Informática, Facultad de Ingeniería, Universidad de Santiago de Chile, Santiago, Chile.
authors.forename: Mario
authors.initials: M
authors.lastname: Inostroza-Ponta
authors.affiliation: Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine (CIBM), Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW, Australia. pablo.moscato@newcastle.edu.au.
authors.forename: Pablo
authors.initials: P
authors.lastname: Moscato
mesh_terms: D000465:Algorithms; D015415:Biomarkers; D019295:Computational Biology / Q000379:methods*; D008962:Models, Theoretical; D059467:Transcriptome / Q000235:genetics*
chemical_list: D015415:Biomarkers
keywords: Clustering; Genetics; Neuroscience; Visualization