Linear Association in Compositional Data Analysis

Egozcue, J. J.; Pawlowsky‐Glahn, Vera; Gloor, Gregory B.

doi:10.17713/ajs.v47i1.689

Cited by 49 publications

(37 citation statements)

References 39 publications

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“…To develop a sparse representation of the principal directions of biological variation in our dataset, we make use of an algorithm for determining a sequence of orthonormal balances that maximize successively the explained variance in a dataset (principal balances) [ 91 ]. As T W , the variation matrix calculated from W is proportional to the Aitchison distance between the bacterial families in our dataset [ 91 , 92 ], applying Ward clustering to the matrix T W results in an approximate solution to the problem of determining principal balances [ 91 , 93 ]. For each posterior sample of W , we calculated T W using Eqs.…”

Section: Methodsmentioning

confidence: 99%

Dynamic linear models guide design and analysis of microbiota studies within artificial human guts

et al. 2018

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BackgroundArtificial gut models provide unique opportunities to study human-associated microbiota. Outstanding questions for these models’ fundamental biology include the timescales on which microbiota vary and the factors that drive such change. Answering these questions though requires overcoming analytical obstacles like estimating the effects of technical variation on observed microbiota dynamics, as well as the lack of appropriate benchmark datasets.ResultsTo address these obstacles, we created a modeling framework based on multinomial logistic-normal dynamic linear models (MALLARDs) and performed dense longitudinal sampling of four replicate artificial human guts over the course of 1 month. The resulting analyses revealed how the ratio of biological variation to technical variation from sample processing depends on sampling frequency. In particular, we find that at hourly sampling frequencies, 76% of observed variation could be ascribed to technical sources, which could also skew the observed covariation between taxa. We also found that the artificial guts demonstrated replicable trajectories even after a recovery from a transient feed disruption. Additionally, we observed irregular sub-daily oscillatory dynamics associated with the bacterial family Enterobacteriaceae within all four replicate vessels.ConclusionsOur analyses suggest that, beyond variation due to sequence counting, technical variation from sample processing can obscure temporal variation from biological sources in artificial gut studies. Our analyses also supported hypotheses that human gut microbiota fluctuates on sub-daily timescales in the absence of a host and that microbiota can follow replicable trajectories in the presence of environmental driving forces. Finally, multiple aspects of our approach are generalizable and could ultimately be used to facilitate the design and analysis of longitudinal microbiota studies in vivo.Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0584-3) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Dynamic linear models guide design and analysis of microbiota studies within artificial human guts

et al. 2018

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“…To develop a sparse representation of the principle directions of biological variation in our dataset, we make use of an algorithm for determining a sequence of orthonormal balances that maximize successively the explained variance in a dataset (principle balances) (94). As { , the variation matrix calculated from , is proportional to the Aitchison distance between the bacterial families in our dataset (94,95) applying 755…”

Section: Hierarchical Clustering Of Variation Matrix 750mentioning

confidence: 99%

Dynamic linear models guide design and analysis of microbiota studies within artificial human guts

Silverman

Durand

Bloom

et al. 2018

Preprint

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We analyzed the artificial gut dataset described below using a MALLARD model that is generative and assumes there exists an unobserved microbial composition ( " ; the state) that evolves through time ( Fig. 1A) due to stochastic biological variations ( " ). 115We regard the state sequence ( $ , … , " , … , ' ) as the true microbial dynamics in a timeseries. Random technical variations ( " ) are then added to the true system state ( " ) resulting in the composition " (Fig. 1B). We observe " through a multinomial counting process. This formulation is similar to the constant level model commonly used in Bayesian time-series analysis (34). By separately modeling the process generating " 120 and " with distinct covariance matrices ( and , respectively), we can decouple biological and technical variations in artificial gut datasets (Fig. 1C). Visually, we found this model provided a good fit to artificial gut data (Fig. S1). Daily and hourly gut microbiota time-series in an artificial human gut 125We applied our model to an artificial gut that was constructed using continuousflow anaerobic bioreactor systems that have been validated as models of human gut microbiota (1,6,15, 35,36). The same starting human fecal inoculum was seeded into replicate ex vivo vessels (n=4) and cultured for 1 month (Fig. 2 and S2). Throughout the experiment pH, temperature, media input rates, and oxygen concentration were all 130 fixed (Methods). To introduce microbial dynamics into our systems, a single bolus of Bacteroides ovatus isolated from the stool donor was administered to the system on Day 23 (Methods). The B. ovatus bolus did not have discernable effects on microbial dynamics, but the media it was suspended in appeared to induce minor shifts in the relative abundances of select bacterial taxa that were visible with hourly sampling (Fig. 135

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“…This dendrogram-like graph shows: (a) the way of grouping parts of the compositional vector; (b) the explanatory role of each sub-composition generated in the partition process; (c) the decomposition of the total variance into balance components associated with each binary partition [27,28]. Before the analysis, removal of rare taxa and substitution of zeros by Bayesian estimation of (non-zero) proportions were performed [35].…”

Section: Discussionmentioning

confidence: 99%

Long-term impact of fecal transplantation in healthy volunteers

Goloshchapov

Olekhnovich

Сидоренко³

et al. 2019

Preprint

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Background: Fecal microbiota transplantation (FMT) is now approved for the treatment of refractory recurrent clostridial colitis, but a number of studies are ongoing in inflammatory bowel diseases, i.e., Crohn's disease, nonspecific ulcerative colitis, and in other autoimmune conditions. In most cases, the effects of FMT are evaluated on patients with initially altered microbiota. The aim of the present study was to evaluate effects of FMT on the gut microbiota composition in healthy volunteers and to track long-term changes. Results:We have performed a combined analysis of three healthy volunteers before and after FMT with frozen capsules, followed by evaluation of their general condition, adverse clinical effects, changes of basic laboratory parameters, and several immune markers. Intestinal microbiota samples were evaluated by 16S rRNA gene sequencing (16S seq) and shotgun sequencing (or whole-genome sequencing -WGS). The data analysis demonstrated the profound shift towards the donor microbiota taxonomic composition in all volunteers. Following FMT, all the volunteers exhibited gut colonization with donor gut bacteria, and persistence of this effect for almost ∼1 year of observation. Transient changes of immune parameters were consistent with suppression of T-cell cytotoxicity. FMT was well tolerated with mild gastrointestinal adverse events and systemic inflammatory response in one volunteer. Conclusions:The FMT procedure leads to significant long-term changes of the gut microbiota in healthy volunteers with the shift towards donor microbiota composition, being relatively safe to the recipients without long-term adverse events.

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Linear Association in Compositional Data Analysis

Cited by 49 publications

References 39 publications

Dynamic linear models guide design and analysis of microbiota studies within artificial human guts

Dynamic linear models guide design and analysis of microbiota studies within artificial human guts

Dynamic linear models guide design and analysis of microbiota studies within artificial human guts

Long-term impact of fecal transplantation in healthy volunteers

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