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Clustering Clinical Data in R

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Mass Spectrometry Data Analysis in Proteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2051))

Abstract

We are currently witnessing a paradigm shift from evidence-based medicine to precision medicine, which has been made possible by the enormous development of technology. The advances in data mining algorithms will allow us to integrate trans-omics with clinical data, contributing to our understanding of pathological mechanisms and massively impacting on the clinical sciences. Cluster analysis is one of the main data mining techniques and allows for the exploration of data patterns that the human mind cannot capture.

This chapter focuses on the cluster analysis of clinical data, using the statistical software, R. We outline the cluster analysis process, underlining some clinical data characteristics. Starting with the data preprocessing step, we then discuss the advantages and disadvantages of the most commonly used clustering algorithms and point to examples of their applications in clinical work. Finally, we briefly discuss how to perform validation of clusters. Throughout the chapter we highlight R packages suitable for each computational step of cluster analysis.

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Authors and Affiliations

  1. Centro de Estudos de Doenças Crónicas (CEDOC), NOVA Medical School-Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Lisbon, Portugal

    Ana Pina & Maria Paula Macedo

  2. ProRegeM PhD Programme, NOVA Medical School/Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Lisbon, Portugal

    Ana Pina

  3. Department of Medical Sciences, Institute of Biomedicine, University of Aveiro, Aveiro, Portugal

    Ana Pina & Maria Paula Macedo

  4. APDP-Diabetes Portugal Education and Research Center (APDP-ERC), Lisbon, Portugal

    Maria Paula Macedo

  5. NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Lisbon, Portugal

    Roberto Henriques

Authors
  1. Ana Pina
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  2. Maria Paula Macedo
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  3. Roberto Henriques
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Corresponding author

Correspondence to Ana Pina .

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Editors and Affiliations

  1. Computational and Experimental Biology Group, CEDOC, Chronic Diseases Research Centre, NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Lisboa, Portugal

    Rune Matthiesen

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Pina, A., Macedo, M.P., Henriques, R. (2020). Clustering Clinical Data in R. In: Matthiesen, R. (eds) Mass Spectrometry Data Analysis in Proteomics. Methods in Molecular Biology, vol 2051. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9744-2_14

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  • DOI: https://doi.org/10.1007/978-1-4939-9744-2_14

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