DOI QR코드

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A guideline for the statistical analysis of compositional data in immunology

  • Yoo, Jinkyung (Department of Statistics, Kyungpook National University) ;
  • Sun, Zequn (Department of Preventive Medicine - Biostatistics, Northwestern University) ;
  • Greenacre, Michael (Department of Economics and Business, Universitat Pompeu Fabra, and Barcelona School of Management) ;
  • Ma, Qin (Department of Biomedical Informatics, The Ohio State University) ;
  • Chung, Dongjun (Department of Biomedical Informatics, The Ohio State University) ;
  • Kim, Young Min (Department of Statistics, Kyungpook National University)
  • 투고 : 2021.12.28
  • 심사 : 2022.04.28
  • 발행 : 2022.07.31

초록

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.

키워드

과제정보

This work was supported by the National Institutes of Health (grant numbers R01-GM122078, R21-CA209848, U01-DA045300) awarded to Dongjun Chung, and the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning(KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20204010600060) awarded to Young Min Kim. The funders had no role in the study design, data collection, and analysis, decision to publish, or preparation of the manuscript.

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