Microbiome Compositional Data Research Articles

Direct interaction network inference for compositional data via codaloss.

16S rRNA gene sequencing and whole microbiome sequencing make it possible and stable to quantitatively analyze the composition of microbial communities and the relationship among microbial communities, microbes, and hosts. One essential step in the analysis of microbiome compositional data is inferring the direct interaction network among microbial species, bringing to light the potential underlying mechanism that regulates interaction in their communities. However, standard statistical analysis may obtain spurious results due to compositional nature of microbiome data; therefore, network recovery of microbial communities remains challenging. Here, we propose a novel loss function called codaloss for direct microbes interaction network estimation under the sparsity assumptions. We develop an alternating direction optimization algorithm to obtain sparse solution of codaloss as estimator. Compared to other state-of-the-art methods, our model makes less assumptions about the microbial networks. The simulation and real microbiome data results show that our method outperforms other methods in network inference. An implementation of codaloss is available from https://github.com/xuebaliang/Codaloss.

LOG-CONTRAST REGRESSION WITH FUNCTIONAL COMPOSITIONAL PREDICTORS: LINKING PRETERM INFANT'S GUT MICROBIOME TRAJECTORIES TO NEUROBEHAVIORAL OUTCOME.

The neonatal intensive care unit (NICU) experience is known to be one of the most crucial factors that drive preterm infant's neurodevelopmental and health outcome. It is hypothesized that stressful early life experience of very preterm neonate is imprinting gut microbiome by the regulation of the so-called brain-gut axis, and consequently, certain microbiome markers are predictive of later infant neurodevelopment. To investigate, a preterm infant study was conducted; infant fecal samples were collected during the infants' first month of postnatal age, resulting in functional compositional microbiome data, and neurobehavioral outcomes were measured when infants reached 36-38 weeks of post-menstrual age. To identify potential microbiome markers and estimate how the trajectories of gut microbiome compositions during early postnatal stage impact later neurobehavioral outcomes of the preterm infants, we innovate a sparse log-contrast regression with functional compositional predictors. The functional simplex structure is strictly preserved, and the functional compositional predictors are allowed to have sparse, smoothly varying, and accumulating effects on the outcome through time. Through a pragmatic basis expansion step, the problem boils down to a linearly constrained sparse group regression, for which we develop an efficient algorithm and obtain theoretical performance guarantees. Our approach yields insightful results in the preterm infant study. The identified microbiome markers and the estimated time dynamics of their impact on the neurobehavioral outcome shed lights on the linkage between stress accumulation in early postnatal stage and neurodevelpomental process of infants.

Compositional knockoff filter for high-dimensional regression analysis of microbiome data.

A critical task in microbiome data analysis is to explore the association between a scalar response of interest and a large number of microbial taxa that are summarized as compositional data at different taxonomic levels. Motivated by fine-mapping of the microbiome, we propose a two-step compositional knockoff filter to provide the effective finite-sample false discovery rate (FDR) control in high-dimensional linear log-contrast regression analysis of microbiome compositional data. In the first step, we propose a new compositional screening procedure to remove insignificant microbial taxa while retaining the essential sum-to-zero constraint. In the second step, we extend the knockoff filter to identify the significant microbial taxa in the sparse regression model for compositional data. Thereby, a subset of the microbes is selected from the high-dimensional microbial taxa as related to the response under a prespecified FDR threshold. We study the theoretical properties of the proposed two-step procedure, including both sure screening and effective false discovery control. We demonstrate these properties in numerical simulation studies to compare our methods to some existing ones and show power gain of the new method while controlling the nominal FDR. The potential usefulness of the proposed method is also illustrated with application to an inflammatory bowel disease data set to identify microbial taxa that influence host geneexpressions.

Variable selection in microbiome compositional data analysis.

Though variable selection is one of the most relevant tasks in microbiome analysis, e.g. for the identification of microbial signatures, many studies still rely on methods that ignore the compositional nature of microbiome data. The applicability of compositional data analysis methods has been hampered by the availability of software and the difficulty in interpreting their results. This work is focused on three methods for variable selection that acknowledge the compositional structure of microbiome data: selbal, a forward selection approach for the identification of compositional balances, and clr-lasso and coda-lasso, two penalized regression models for compositional data analysis. This study highlights the link between these methods and brings out some limitations of the centered log-ratio transformation for variable selection. In particular, the fact that it is not subcompositionally consistent makes the microbial signatures obtained from clr-lasso not readily transferable. Coda-lasso is computationally efficient and suitable when the focus is the identification of the most associated microbial taxa. Selbal stands out when the goal is to obtain a parsimonious model with optimal prediction performance, but it is computationally greedy. We provide a reproducible vignette for the application of these methods that will enable researchers to fully leverage their potential in microbiome studies.

The Community Simulator: A Python package for microbial ecology.

Natural microbial communities contain hundreds to thousands of interacting species. For this reason, computational simulations are playing an increasingly important role in microbial ecology. In this manuscript, we present a new open-source, freely available Python package called Community Simulator for simulating microbial population dynamics in a reproducible, transparent and scalable way. The Community Simulator includes five major elements: tools for preparing the initial states and environmental conditions for a set of samples, automatic generation of dynamical equations based on a dictionary of modeling assumptions, random parameter sampling with tunable levels of metabolic and taxonomic structure, parallel integration of the dynamical equations, and support for metacommunity dynamics with migration between samples. To significantly speed up simulations using Community Simulator, our Python package implements a new Expectation-Maximization (EM) algorithm for finding equilibrium states of community dynamics that exploits a recently discovered duality between ecological dynamics and convex optimization. We present data showing that this EM algorithm improves performance by between one and two orders compared to direct numerical integration of the corresponding ordinary differential equations. We conclude by listing several recent applications of the Community Simulator to problems in microbial ecology, and discussing possible extensions of the package for directly analyzing microbiome compositional data.

Biomarkers for oralization during long-term proton pump inhibitor therapy predict survival in cirrhosis

Proton pump inhibitors (PPI) are an invaluable therapy option for acid related diseases; however, PPI therapy is also linked to a series of side effects in cirrhosis, such as microbiome alterations, spontaneous bacterial peritonitis and hepatic encephalopathy. Decision tools to balance benefits and risks of PPI therapy are largely missing. In this study, we tested gut-derived biomarkers to identify PPI-associated dysbiosis, its association with gut barrier function and liver-related mortality. In this observational study, faecal microbiome composition data obtained from 16S rDNA sequencing of 90 cirrhotic patients with and without long-term PPI use and additional potential biomarkers identified from the literature were evaluated for their predictive value regarding PPI-associated dysbiosis and liver-related three-year mortality. In addition, faecal calprotectin, faecal zonulin and serum lipopolysaccharides were assessed as markers for intestinal inflammation, gut permeability and bacterial translocation. Streptococcus salivarius, Veillonella parvula and the genus Streptococcus were significantly increased in patients with long-term PPI therapy and performed well as biomarkers for PPI-associated dysbiosis (accuracy: 74%, 72% and 74%, respectively). The abundance of Streptococcus salivarius was linked to intestinal inflammation and gut barrier dysfunction, whereas the abundance of Veillonella parvula showed associations with liver disease severity; both were independent predictors for liver-related three-year mortality. Gut-derived biomarkers of PPI-associated dysbiosis are linked to worse outcome and a potential option to evaluate the risks of adverse events during long-term PPI therapy.

Estimating and testing the microbial causal mediation effect with high-dimensional and compositional microbiome data.

Recent microbiome association studies have revealed important associations between microbiome and disease/health status. Such findings encourage scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, and have led to applying statistical models to quantify causal microbiome effects and to identify the specific microbial agents. However, there are no existing causal mediation methods specifically designed to handle high dimensional and compositional microbiome data. We propose a rigorous Sparse Microbial Causal Mediation Model (SparseMCMM) specifically designed for the high dimensional and compositional microbiome data in a typical three-factor (treatment, microbiome and outcome) causal study design. In particular, linear log-contrast regression model and Dirichlet regression model are proposed to estimate the causal direct effect of treatment and the causal mediation effects of microbiome at both the community and individual taxon levels. Regularization techniques are used to perform the variable selection in the proposed model framework to identify signature causal microbes. Two hypothesis tests on the overall mediation effect are proposed and their statistical significance is estimated by permutation procedures. Extensive simulated scenarios show that SparseMCMM has excellent performance in estimation and hypothesis testing. Finally, we showcase the utility of the proposed SparseMCMM method in a study which the murine microbiome has been manipulated by providing a clear and sensible causal path among antibiotic treatment, microbiome composition and mouse weight. https://sites.google.com/site/huilinli09/software and https://github.com/chanw0/SparseMCMM. Supplementary data are available at Bioinformatics online.

Historical DNA Metabarcoding of the Prey and Microbiome of Trematomid Fishes Using Museum Samples

Antarctic specimens collected during various research expeditions are preserved in natural history collections around the world potentially offering a cornucopia of morphological and molecular data. Historical samples of marine species are, however, often preserved in formaldehyde which may render them useless for genetic analysis. We sampled stomachs and hindguts from 225 Trematomus specimens from the Natural History Museum London. These samples were initially collected between 20 and 100 years ago and fixed in either formaldehyde or ethanol. A 313 bp fragment of the cytochrome c oxidase subunit I (COI) was amplified and sequenced for prey item identification in the stomach and a 450 bp region of the 16S rRNA gene to investigate microbiome composition in the gut system. Both data sets were characterized by large dropout rates during extensive quality controls. Eventually, no unambiguous results regarding stomach content (COI) were retained, possibly due to degraded DNA, inefficient primers and contamination. In contrast, reliable microbiome composition data (16S rRNA) was obtained from 26 samples. These data showed a correlation in change of microbiome composition with fish size as well as year of the catch, indicating a microbiome shift throughout ontogeny and between samples from different decades. A comparison with contemporary samples indicated that the intestinal microbiome of Trematomus may have drastically changed within the last century. Facilitating molecular analyses of museum stored fish holds enormous potential for microevolutionary insights that can benefit current efforts to prioritize conservation units in the Southern Ocean.

Generalized linear models with linear constraints for microbiome compositional data.

Motivated by regression analysis for microbiome compositional data, this article considers generalized linear regression analysis with compositional covariates, where a group of linear constraints on regression coefficients are imposed to account for the compositional nature of the data and to achieve subcompositional coherence. A penalized likelihood estimation procedure using a generalized accelerated proximal gradient method is developed to efficiently estimate the regression coefficients. A de-biased procedure is developed to obtain asymptotically unbiased and normally distributed estimates, which leads to valid confidence intervals of the regression coefficients. Simulations results show the correctness of the coverage probability of the confidence intervals and smaller variances of the estimates when the appropriate linear constraints are imposed. The methods are illustrated by a microbiome study in order to identify bacterial species that are associated with inflammatory bowel disease (IBD) and to predict IBD using fecal microbiome.

A marginalized two-part Beta regression model for microbiome compositional data.

In microbiome studies, an important goal is to detect differential abundance of microbes across clinical conditions and treatment options. However, the microbiome compositional data (quantified by relative abundance) are highly skewed, bounded in [0, 1), and often have many zeros. A two-part model is commonly used to separate zeros and positive values explicitly by two submodels: a logistic model for the probability of a specie being present in Part I, and a Beta regression model for the relative abundance conditional on the presence of the specie in Part II. However, the regression coefficients in Part II cannot provide a marginal (unconditional) interpretation of covariate effects on the microbial abundance, which is of great interest in many applications. In this paper, we propose a marginalized two-part Beta regression model which captures the zero-inflation and skewness of microbiome data and also allows investigators to examine covariate effects on the marginal (unconditional) mean. We demonstrate its practical performance using simulation studies and apply the model to a real metagenomic dataset on mouse skin microbiota. We find that under the proposed marginalized model, without loss in power, the likelihood ratio test performs better in controlling the type I error than those under conventional methods.

Alterations of the Gut Microbiota in Hashimoto's Thyroiditis Patients.

Hashimoto's thyroiditis (HT) is an organ-specific autoimmune disease in which both genetic predisposition and environmental factors serve as disease triggers. Many studies have indicated that alterations in the gut microbiota are important environmental factors in the development of inflammatory and autoimmune diseases. A comparative analysis was systematically performed of the gut microbiota in HT patients and healthy controls. First, a cross-sectional study of 28 HT patients and 16 matched healthy controls was conducted. Fecal samples were collected, and microbiota were analyzed using 16S ribosomal RNA gene sequencing. Second, an independent cohort of 22 HT patients and 11 healthy controls was used to evaluate the diagnostic potential of the selected biomarkers. Similar levels of bacterial richness and diversity were found in the gut microbiota of HT patients and healthy controls (p = 0.11). A detailed fecal microbiota Mann-Whitney U-test (Q value <0.05) revealed that the abundance levels of Blautia, Roseburia, Ruminococcus_torques_group, Romboutsia, Dorea, Fusicatenibacter, and Eubacterium_hallii_group genera were increased in HT patients, whereas the abundance levels of Fecalibacterium, Bacteroides, Prevotella_9, and Lachnoclostridium genera were decreased. A correlation matrix based on the Spearman correlation distance confirmed correlations among seven clinical parameters. Additionally, the linear discriminant analysis effect size method showed significant differences in 27 genera between the two groups that were strongly correlated with clinical parameters. The linear discriminant analysis value was used to select the first 10 species from the 27 different genera as biomarkers, achieving area under the curve values of 0.91 and 0.88 for exploration and validation data, respectively. Characterization of the gut microbiota in HT patients confirmed that HT patients have altered gut microbiota and that gut microbiota are correlated with clinical parameters, suggesting that microbiome composition data could be used for disease diagnosis. Further investigation is required to understand better the role of the gut microbiota in the pathogenesis of HT.

A general framework for association analysis of microbial communities on a taxonomic tree.

MotivationAssociation analysis of microbiome composition with disease-related outcomes provides invaluable knowledge towards understanding the roles of microbes in the underlying disease mechanisms. Proper analysis of sparse compositional microbiome data is challenging. Existing methods rely on strong assumptions on the data structure and fail to pinpoint the associated microbial communities.ResultsWe develop a general framework to: (i) perform robust association tests for the microbial community that exhibits arbitrary inter-taxa dependencies; (ii) localize lineages on the taxonomic tree that are associated with covariates (e.g. disease status); and (iii) assess the overall association of the whole microbial community with the covariates. Unlike existing methods for microbiome association analysis, our framework does not make any distributional assumptions on the microbiome data; it allows for the adjustment of confounding variables and accommodates excessive zero observations; and it incorporates taxonomic information. We perform extensive simulation studies under a wide-range of scenarios to evaluate the new methods and demonstrate substantial power gain over existing methods. The advantages of the proposed framework are further demonstrated with real datasets from two microbiome studies. The relevant R package miLineage is publicly available.Availability and ImplementationmiLineage package, manual and tutorial are available at https://medschool.vanderbilt.edu/tang-lab/software/miLineage.Supplementary information Supplementary data are available at Bioinformatics online.

A two-part mixed-effects model for analyzing longitudinal microbiome compositional data.

The human microbial communities are associated with many human diseases such as obesity, diabetes and inflammatory bowel disease. High-throughput sequencing technology has been widely used to quantify the microbial composition in order to understand its impacts on human health. Longitudinal measurements of microbial communities are commonly obtained in many microbiome studies. A key question in such microbiome studies is to identify the microbes that are associated with clinical outcomes or environmental factors. However, microbiome compositional data are highly skewed, bounded in [0,1), and often sparse with many zeros. In addition, the observations from repeated measures in longitudinal studies are correlated. A method that takes into account these features is needed for association analysis in longitudinal microbiome data. In this paper, we propose a two-part zero-inflated Beta regression model with random effects (ZIBR) for testing the association between microbial abundance and clinical covariates for longitudinal microbiome data. The model includes a logistic regression component to model presence/absence of a microbe in the samples and a Beta regression component to model non-zero microbial abundance, where each component includes a random effect to account for the correlations among the repeated measurements on the same subject. Both simulation studies and the application to real microbiome data have shown that ZIBR model outperformed the previously used methods. The method provides a useful tool for identifying the relevant taxa based on longitudinal or repeated measures in microbiome research. https://github.com/chvlyl/ZIBR CONTACT: hongzhe@upenn.edu.