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Assessing the Microbiome—Current and Future Technologies and Applications
Published in David Perlmutter, The Microbiome and the Brain, 2019
Thomas Gurry, Shrish Budree, Alim Ladha, Bharat Ramakrishna, Zain Kassam
On the other hand, beta diversity is an analysis technique used to compare diversity between samples (Jovel et al. 2016 and Olsen 2016). It is typically used to determine “how different” samples are from each other by effectively measuring the distance between samples because similar samples are “closer” together. This technique can be done with the supervision of phylogenetic data (e.g., UniFrac) or without it (e.g., Bray–Curtis dissimilarity). Once the beta diversity is computed, it is often displayed graphically by reducing the dimensionality of the dataset using either non-metric multidimensional scaling or principal coordinate analysis. These methods are extremely useful for both data visualization and clustering based on the covariates under investigation, but both rely on the assumption that variation in beta diversity can be explained by a few independent factors.
Composition and Diversity of Human Oral Microbiome
Published in Chaminda Jayampath Seneviratne, Microbial Biofilms, 2017
Preethi Balan, Chaminda Jayampath Seneviratne and Wim Crielaard
Comprehensive reports from several sequencing studies have shown that each body habitat has a unique microbial community because every human body surface has a unique environment that shapes niche-specific microbiota. According to Whittaker, the total species diversity in an environment (gamma diversity) is reliant on two factors, namely, the mean species diversity in a particular site or habitat, known as alpha diversity, and the species differentiation between those habitats or sites, known as beta diversity. As compared to all other human microbial habitats, the oral cavity is unique owing to the presence of two types of microbial colonisation sites: shedding surfaces (mucosa) and non-shedding surfaces (teeth or dentures) [18,45]. The oral microbiota preferentially colonises the different habitats in the oral cavity depending on the optimal conditions each niche offers to the populating microbes [46].
Human Gut Microbiota–Transplanted Gn Pig Models for HRV Infection
Published in Lijuan Yuan, Vaccine Efficacy Evaluation, 2022
Measurements of alpha diversity in HHGM pigs were significantly lower than those in UHGM pigs at PID 28 and PCD 7. In addition, alpha diversity measurements decreased in HHGM pigs from PID28 to PCD7. There were no significant differences before or after challenge for the UHGM pigs. These results suggest that VirHRV challenge caused a greater disruption to the microbiota in HHGM pigs than in UHGM pigs. Beta diversity analysis was visualized with a PCoA plot based on unweighted UniFrac. Regardless of the time point, the microbiota from pigs with HHGM clustered in one group while samples from UHGM pigs formed another group (Figure 11.9).
Distinct profiles of bile acid metabolism caused by gut microbiota in kidney transplantation recipients revealed by 16S rRNA gene sequencing
Published in Archives of Physiology and Biochemistry, 2023
Xiaoqiang Wu, Xiangyong Tian, Guanghui Cao, Zhiwei Wang, Xuan Wu, Yue Gu, Tianzhong Yan
Beta diversity refers to the diversity of species composition or the replacement rate of species along the environmental gradient between different communities that change along the environmental gradient, so it is also called between-habitat diversity (Walters and Martiny 2020). Beta diversity reflects the degree of similarity in species diversity of different sample groups, and the small value of beta diversity indicated that the species of the two groups were similar. When considering the existence of species, there is a difference in microbial species between the healthy control group and the renal transplant recipient, but it is not significant, with unweighted UniFrac distances (R2 =0.08162; P = 0.4929). When considering species abundance, we find that there were significant changes in the community structure of bacteria of experiment recipients compared to normal control, with weighted UniFrac accommodate (R2=0.2228; p = 0.0453) (Figure 4).
A spatio-temporal analysis of marine diatom communities associated with pristine and aged plastics
Published in Biofouling, 2023
Olivier Laroche, Olga Pantos, Joanne M. Kingsbury, Anastasija Zaiko, Jessica Wallbank, Gavin Lear, Jacob Thompson-Laing, Francois Audrezet, Stefan Maday, Fraser Doake, Robert Abbel, Maxime Barbier, Hayden Masterton, Regis Risani, Dawn Smith, Beatrix Theobald, Louise Weaver, Xavier Pochon
Beta-diversity was visualized with Principal Coordinate Analysis (PCoA). The effect of submersion time, depth, location, substrate type and state, including all interaction terms, were tested for each deployment with a permutational analysis of variance (PERMANOVA; permutations = 999, by = ‘terms’) with the ‘adonis2’ function of the vegan R package (version 2.6.2; Oksanen et al. 2020). A similar test was performed per location and submersion time with PERMANOVA set by ‘margin’ to better understand the independent role of each factor, including depth, through time. This change was subsequently visualized with regression plots using ggplot2. All beta-diversity analyses were performed on rarefied, centered-log ratio (clr) transformed data and the Aitchison distance (the Euclidean distance from clr transformed data), following recommendations from Gloor et al. (2017) when dealing with compositional data.
Characterizing the gut microbiota in adults with bipolar disorder: a pilot study
Published in Nutritional Neuroscience, 2021
Roger S. McIntyre, Mehala Subramaniapillai, Margarita Shekotikhina, Nicole E. Carmona, Yena Lee, Rodrigo B. Mansur, Elisa Brietzke, Dominika Fus, Alexandria S. Coles, Michelle Iacobucci, Caroline Park, Ryan Potts, Merwa Amer, Jessica Gillard, Cindy James, Rebecca Anglin, Michael G. Surette
The data were investigated for significant differences in alpha and beta diversity between BD-I and BD-II subjects, and HCs. Measures of alpha diversity estimate the diversity within a grouping (in this case, diagnostic groupings of study participants) while measures of beta diversity compare diversity between these groups. These analyses were followed by an investigation of the composition of the microbiota and individual taxa, distinguishing the diagnostic groups. Alpha diversity was assessed using measures of both richness (a measure of the number of organisms in a sample) and evenness (a measure of the differing abundance of organisms in a sample) of the participant samples. The foregoing estimates were generated first by rarifying (through sampling without replacement) the number of reads in each sample to the number of reads in the smallest sample (i.e. 13,811). This process was repeated 100 times and richness and evenness for each sample was averaged across the 100 repeats. Differences were tested for statistical significance using a Kruskall–Wallis test.