Microbiome data analysis with applications to pre-clinical studies using QIIME2: Statistical considerations

Shesh N Rai; Chen Qian; Jianmin Pan; Jayesh P Rai; Ming Song; Juhi Bagaitkar; Michael Merchant; Matthew Cave; Nejat K Egilmez; Craig J McClain

doi:10.1016/j.gendis.2019.12.005

Microbiome data analysis with applications to pre-clinical studies using QIIME2: Statistical considerations

Genes Dis. 2019 Dec 24;8(2):215-223. doi: 10.1016/j.gendis.2019.12.005. eCollection 2021 Mar.

Authors

Shesh N Rai^{1

2

3

4}, Chen Qian^{1

2}, Jianmin Pan¹, Jayesh P Rai^{1

2}, Ming Song^{3

4

5}, Juhi Bagaitkar⁶, Michael Merchant^{3

4

5}, Matthew Cave^{3

4

5

7}, Nejat K Egilmez⁸, Craig J McClain^{3

4

5

7}

Affiliations

¹ Biostatistics and Bioinformatics Facility, James Graham Brown Cancer Center, University of Louisville, Louisville, KY, 40202, USA.
² Department of Biostatistics and Bioinformatics, University of Louisville, Louisville, KY, 40202, USA.
³ University of Louisville Alcohol Research Center, University of Louisville, Louisville, KY, 40202, USA.
⁴ University of Louisville Hepatobiology & Toxicology Center, University of Louisville, Louisville, KY, 40202, USA.
⁵ Department of Medicine, University of Louisville, Louisville, KY, 40202, USA.
⁶ Department of Oral Immunology & Infectious Diseases, University of Louisville, Louisville, KY, 40202, USA.
⁷ Robley Rex Louisville VAMC, Louisville, KY, 40206, USA.
⁸ Department of Microbiology & Immunology, University of Louisville, Louisville, KY, 40202, USA.

Abstract

Diversity analysis and taxonomic profiles can be generated from marker-gene sequence data with the help of many available computational tools. The Quantitative Insights into Microbial Ecology Version 2 (QIIME2) has been widely used for 16S rRNA data analysis. While many articles have demonstrated the use of QIIME2 with suitable datasets, the application to pre-clinical data has rarely been talked about. The issues involved in the pre-clinical data include the low-quality score and small sample size that should be addressed properly during analysis. In addition, there are few articles that discuss the detailed statistical methods behind those alpha and beta diversity significance tests that researchers are eager to find. Running the program without knowing the logic behind it is extremely risky. In this article, we first provide a guideline for analyzing 16S rRNA data using QIIME2. Then we will talk about issues in pre-clinical data, and how they could impact the outcome. Finally, we provide brief explanations of statistical methods such as group significance tests and sample size calculation.

Keywords: 16S rRNA gene; ANOVA; Alpha diversity; Beta diversity; Bioinformatics; Microbiome data; QIIME; Sample size calculation.