A guideline for the statistical analysis of compositional data in immunology

Jinkyung Yoo, Zequn Sun, Michael Greenacre, Qin Ma, Dongjun Chung*, Young Min Kim

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The study of immune cellular composition has been of great scientific interest in immunology because of the generation of multiple large-scale data. From the statistical point of view, such immune cellular data should be treated as compositional. In compositional data, each element is positive, and all the elements sum to a constant, which can be set to one in general. Standard statistical methods are not directly applicable for the analysis of compositional data because they do not appropriately handle correlations between the compositional elements. In this paper, we review statistical methods for compositional data analysis and illustrate them in the context of immunology. Specifically, we focus on regression analyses using log-ratio transformations and the alternative approach using Dirichlet regression analysis, discuss their theoretical foundations, and illustrate their applications with immune cellular fraction data generated from colorectal cancer patients.

Original languageEnglish (US)
Pages (from-to)453-469
Number of pages17
JournalCommunications for Statistical Applications and Methods
Issue number4
StatePublished - 2022


  • Compositional data
  • Compositional regression
  • Dirichlet regression
  • Immuno-oncology
  • Immunology
  • Log-ratio transformation

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Finance
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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