A guideline for the statistical analysis of compositional data in immunology

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

doi:10.29220/CSAM.2022.29.4.453

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

Preventive Medicine

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	453-469
Number of pages	17
Journal	Communications for Statistical Applications and Methods
Volume	29
Issue number	4
DOIs	https://doi.org/10.29220/CSAM.2022.29.4.453
State	Published - 2022

Funding

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. 1 Corresponding author: Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA. E-mail: [email protected] 2Corresponding author: Department of Statistics, Kyungpook National University, Daegu, 41566, South Korea. E-mail: [email protected]

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.29220/CSAM.2022.29.4.453

Cite this

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abstract = "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.",

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AU - Greenacre, Michael

AU - Ma, Qin

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AU - Kim, Young Min

PY - 2022

Y1 - 2022

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AB - 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.

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A guideline for the statistical analysis of compositional data in immunology

Abstract

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

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