Analysis of correlation structures in renal cell carcinoma patient data

Italo Zoppis, Massimiliano Borsani, Erica Gianazza, Clizia Chinello, Francesco Rocco, Giancarlo Albo, André M. Deelder, Yuri E M Van Der Burgt, Marco Antoniotti, Fulvio Magni, Giancarlo Mauri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Mass Spectrometry (MS)-based technologies represent a promising area of research in clinical analysis. They are primarily concerned with measuring the relative intensity (abundance) of many protein/peptide molecules associated with their mass-to-charge ratios over a particular range of molecular masses. These measurements (generally referred as proteomic signals or spectra) constitute a huge amount of information which requires adequate tools to be investigated and interpreted. Following the methodology for testing hypotheses, we investigate the proteomic signals of the most common type of Renal Cell Carcinoma, the Clear Cell variant (ccRCC). Specifically, the aim of our investigation is to detect changes of the signal correlations from control to case group (ccRCC or non-ccRCC). To this end, we sample and represent each population group through a graph providing, as it will be defined below, the observed signal correlation structure. This way, graphs establish abstract frames of reference in our analysis giving the opportunity to test hypotheses over their properties. In other terms, changes are detected by testing graph property modifications from group to group. We show the results by reporting the mass-to-charge values which identify bounded regions where changes have been detected. The main interest in handling these regions is to perceive which signal ranges are associated with some specific factors of interest (e.g., studying differentially expressed peaks between case and control groups) and thus, to suggest potential biomarkers for future analysis or for clinical monitoring. Data were collected, from patients and healthy volunteers at the Ospedale Maggiore Policlinico Foundation (Milano, Italy).

Original languageEnglish
Title of host publicationBIOINFORMATICS 2012 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms
Pages251-256
Number of pages6
Publication statusPublished - 2012
EventInternational Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2012 - Vilamoura, Algarve, Portugal
Duration: Feb 1 2012Feb 4 2012

Other

OtherInternational Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2012
CountryPortugal
CityVilamoura, Algarve
Period2/1/122/4/12

Fingerprint

Correlation Structure
Cells
Cell
Molecular mass
Testing
Biomarkers
Peptides
Mass spectrometry
Proteomics
Proteins
Graph in graph theory
Molecules
Charge
Monitoring
Testing Hypotheses
Hypothesis Test
Mass Spectrometry
Range of data
Protein
Methodology

Keywords

  • Bipartite graphs
  • Clinical analysis
  • Correlation
  • Hypotheses testing
  • Mass spectrometry
  • Proteomics

ASJC Scopus subject areas

  • Modelling and Simulation

Cite this

Zoppis, I., Borsani, M., Gianazza, E., Chinello, C., Rocco, F., Albo, G., ... Mauri, G. (2012). Analysis of correlation structures in renal cell carcinoma patient data. In BIOINFORMATICS 2012 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (pp. 251-256)

Analysis of correlation structures in renal cell carcinoma patient data. / Zoppis, Italo; Borsani, Massimiliano; Gianazza, Erica; Chinello, Clizia; Rocco, Francesco; Albo, Giancarlo; Deelder, André M.; Van Der Burgt, Yuri E M; Antoniotti, Marco; Magni, Fulvio; Mauri, Giancarlo.

BIOINFORMATICS 2012 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms. 2012. p. 251-256.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zoppis, I, Borsani, M, Gianazza, E, Chinello, C, Rocco, F, Albo, G, Deelder, AM, Van Der Burgt, YEM, Antoniotti, M, Magni, F & Mauri, G 2012, Analysis of correlation structures in renal cell carcinoma patient data. in BIOINFORMATICS 2012 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms. pp. 251-256, International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2012, Vilamoura, Algarve, Portugal, 2/1/12.
Zoppis I, Borsani M, Gianazza E, Chinello C, Rocco F, Albo G et al. Analysis of correlation structures in renal cell carcinoma patient data. In BIOINFORMATICS 2012 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms. 2012. p. 251-256
Zoppis, Italo ; Borsani, Massimiliano ; Gianazza, Erica ; Chinello, Clizia ; Rocco, Francesco ; Albo, Giancarlo ; Deelder, André M. ; Van Der Burgt, Yuri E M ; Antoniotti, Marco ; Magni, Fulvio ; Mauri, Giancarlo. / Analysis of correlation structures in renal cell carcinoma patient data. BIOINFORMATICS 2012 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms. 2012. pp. 251-256
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