Identification of the Unchanging Reference Component of Compositional Data from the Properties of the Coefficient of Variation

Ohta, Tohru; Arai, Hiroyoshi; Noda, Atsushi

doi:10.1007/s11004-011-9332-y

Cited by 4 publications

(22 citation statements)

References 12 publications

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“…The most unchanging of the four components was determined to be C4, following the method of Ohta et al . (2011). Based on this result, the additive logratios of the proportions of C1, C2 and C3 to that of C4 for each sample [ALR(C1), ALR(C2) and ALR(C3), respectively] were calculated from Eq.…”

Section: Resultsmentioning

confidence: 99%

“…In the present case, it is possible that C2 and C3 had a similar fluctuation of sediment fluxes relative to C4, which may have resulted in overly concentrated distributions. In the present study, however, the most unchanging component (C4) was selected according to statistical methods (Ohta et al ., 2011) and was used as a reference component for logratio transformation. Therefore, given the clearly different variability of the logratio distributions, it is reasonable to interpret the sediment fluxes of C1 as having clearly more variability than those of C2 to C4, although relatively.…”

Section: Discussionmentioning

confidence: 99%

“…To discuss the behaviour of each component using ALR, it is important to select an unchanging component as a reference. A suitable unchanging component as a reference was selected following the method of Ohta et al (2011). This method allows for the estimation of the unchanging component based on the properties of the coefficient of variation of component ratios through two tests.…”

Section: Analysis Of Grain-size Distributionsmentioning

confidence: 99%

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Logratio analysis of components separated from grain‐size distributions and implications for sedimentary processes: An example of bottom surface sediments in a shallow lake

Yamaguchi,

Ando,

Enokida

et al. 2024

Sedimentology

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The grain‐size distributions of sediments can yield important information about sediment provenance and sedimentary processes; however, grain‐size distributions are frequently polymodal, rendering analyses difficult. To improve analyses of polymodal grain‐size data, the present study decomposed the grain‐size distributions of bottom surface sediments from Lake Kitaura, a shallow lake in Japan, into lognormal distributions and performed logratio analysis of their mixing proportions. The polymodal grain‐size distributions of the studied samples were separated into four common components at most sites. This logratio analysis revealed clear differences in the characteristics of the spatial distributions of the separated grain‐size components. The logratio values indicated that the three finer components were uniformly deposited within the lake, whereas the coarsest component was spatially diverse, reflecting differences in their sources and sedimentary processes. These results demonstrate the effectiveness of decomposition and logratio analysis of polymodal grain‐size distributions for estimating sedimentary processes. This method can be applied to modern sediments and for palaeoenvironmental reconstructions using sediment cores.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Analysis Of Grain-size Distributionsmentioning

confidence: 99%

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Logratio analysis of components separated from grain‐size distributions and implications for sedimentary processes: An example of bottom surface sediments in a shallow lake

Yamaguchi,

Ando,

Enokida

et al. 2024

Sedimentology

Self Cite

View full text Add to dashboard Cite

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“…Basis is a non-constrained data (counting data) [1][2][3][4][5]. Composition corresponds to the constrained data [1][2][3][4][5]. Compositional data is relatively normalized data.…”

Section: Introductionmentioning

confidence: 99%

“…When considering the ratios of compositions, the coefficient of variation is affected by moments up to the fourth order of basis. The coefficient of variation of ratios is subject to change when the unchanging component is switched between the denominator and numerator [1]. Compositional data cannot be used to calculate sums, differences, products, or quotients [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Diversity of the Japanese Gut Microbiome Analysis: Relative Approach Using Principal Component Analysis

Itagaki,

Sakata,

Hasebe

et al. 2024

Preprint

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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.

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Diversity of the Japanese Gut Microbiome

Itagaki,

Sakata,

Hasebe

et al. 2024

Preprint

View full text Add to dashboard Cite

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). However, misinformation is pervasive in the human microbiome literature and 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 alpha diversity.

show abstract

Identification of the Unchanging Reference Component of Compositional Data from the Properties of the Coefficient of Variation

Cited by 4 publications

References 12 publications

Logratio analysis of components separated from grain‐size distributions and implications for sedimentary processes: An example of bottom surface sediments in a shallow lake

Logratio analysis of components separated from grain‐size distributions and implications for sedimentary processes: An example of bottom surface sediments in a shallow lake

Diversity of the Japanese Gut Microbiome Analysis: Relative Approach Using Principal Component Analysis

Diversity of the Japanese Gut Microbiome

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