Version 1
: Received: 2 February 2024 / Approved: 5 February 2024 / Online: 5 February 2024 (13:17:52 CET)
Version 2
: Received: 19 February 2024 / Approved: 19 February 2024 / Online: 19 February 2024 (15:27:25 CET)
Version 3
: Received: 11 March 2024 / Approved: 11 March 2024 / Online: 11 March 2024 (16:59:03 CET)
How to cite:
Itagaki, T.; Sakata, K.-I.; Hasebe, A.; Kitagawa, Y. Diversity of the Japanese Gut Microbiome Analysis: Relative Approach Using Principal Component Analysis. Preprints2024, 2024020275. https://doi.org/10.20944/preprints202402.0275.v3
Itagaki, T.; Sakata, K.-I.; Hasebe, A.; Kitagawa, Y. Diversity of the Japanese Gut Microbiome Analysis: Relative Approach Using Principal Component Analysis. Preprints 2024, 2024020275. https://doi.org/10.20944/preprints202402.0275.v3
Itagaki, T.; Sakata, K.-I.; Hasebe, A.; Kitagawa, Y. Diversity of the Japanese Gut Microbiome Analysis: Relative Approach Using Principal Component Analysis. Preprints2024, 2024020275. https://doi.org/10.20944/preprints202402.0275.v3
APA Style
Itagaki, T., Sakata, K. I., Hasebe, A., & Kitagawa, Y. (2024). Diversity of the Japanese Gut Microbiome Analysis: Relative Approach Using Principal Component Analysis. Preprints. https://doi.org/10.20944/preprints202402.0275.v3
Chicago/Turabian Style
Itagaki, T., Akira Hasebe and Yoshimasa Kitagawa. 2024 "Diversity of the Japanese Gut Microbiome Analysis: Relative Approach Using Principal Component Analysis" Preprints. https://doi.org/10.20944/preprints202402.0275.v3
Abstract
A compositional data vector is a special type of multivariate observation in which the elements of the vector are non-negative and sum to a constant, usually taken to be unity. A compositional data does not have zero and only retains relative information. Furthermore, comparisons can only be made between compositional data of the same component. The relevant sample space is the standard simplex. A simplex space is a space that is a generalized form of a triangle. For compositional data, many of the operations defined in Euclidean space are meaningless. Microbiome analyzes have become popular in recent years. Operational Taxonomic Units (OTU) and Amplicon Sequence Variants (ASVs) used in microbiome analysis are one type of compositional data. The microbiome data are counts of different species within a sample and it is compositional. Although there is an order of bacterial abundance within a sample, there is no order of bacterial abundance between samples. Firmicutes and Bacteroidetes, the major phyla in the colon, have been observed in humans worldwide. Gut microbiome analyses often use the Firmicutes/Bacteroidetes ratio and principal coordinates analysis (PCoA). Alpha and beta diversities are used as indicators of bacterial flora diversity. However, misinformation is pervasive in the human microbiome literature and analysis. There is a lot of misunderstanding regarding compositional data and its analysis. An attempt was made to demonstrate how to analyze using the “National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN JMD) (Public Data).” The results showed that PCoA did not work, and principal component analysis (PCA) was useful for analyzing the gut microbiome relative diversity.
Keywords
gut microbiome; compositional data; principal component analysis; unsupervised machine learning; diversity
Subject
Biology and Life Sciences, Biology and Biotechnology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.