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Over the last ten years, there has been considerable progress in using digital behavioral phenotypes, captured passively and continuously from smartphones and wearable devices, to infer mood and diagnose major depressive disorder. However, most digital phenotype studies suffer from poor replicability, often fail to detect clinically relevant events, and use measures of depression that are not validated or suitable for collecting large and longitudinal data. Here, we report high-quality longitudinal validated assessments of mood from computerized adaptive testing paired with continuous digital assessments of behavior from smartphone sensors for up to 40 weeks on 183 individuals experiencing mild to severe symptoms of depression. We apply a novel combination of cubic spline interpolation and idiographic models to generate individualized predictions of future mood from the digital behavioral phenotypes, achieving high prediction accuracy of depression severity up to three weeks in advance (R2≥ 80%). We show that the passive behavioral phenotypes enhance prediction of future mood over and above a baseline model which predicts future mood based on past depression severity alone for 52% of individuals in our cohort. In conclusion, our study verified the feasibility of obtaining high-quality longitudinal assessments of mood from a clinical population and predicting symptom severity weeks in advance using passively collected digital behavioral data. Our results indicate the possibility of expanding the repertoire of patient-specific behavioral measures to enable future psychiatric research.

Early microbiota perturbations are associated with disorders that involve immunological underpinnings. Cesarean section (CS)-born babies show altered microbiota development in relation to babies born vaginally. Here we present the first statistically powered longitudinal study to determine the effect of restoring exposure to maternal vaginal fluids after CS birth., This work was partially supported by the C&D Research Fund, the Emch Fund for Human Microbiome Studies, and CIFAR FS20-078 #125869 (to M.G.D.-B.); Chilean CONICYT PIA/ANILLO grant ACT172097 and Chilean SOCHIPE Project 022019 (to P.R.H.); and the Norwegian Institute of Public Health (2019-0350), the Emerald Foundation, an NIH Pioneer award (1DP1AT010885), the National Institute of Justice (2016-DN-BX-4194), the San Diego Digestive Diseases Research Center (NIDDK 1P30DK120515), Janssen Pharmaceuticals (20175015)

A major challenge of analyzing the compositional structure of microbiome data is identifying its potential origins. Here, we introduce fast expectation-maximization microbial source tracking (FEAST), a ready-to-use scalable framework that can simultaneously estimate the contribution of thousands of potential source environments in a timely manner, thereby helping unravel the origins of complex microbial communities ( https://github.com/cozygene/FEAST ). The information gained from FEAST may provide insight into quantifying contamination, tracking the formation of developing microbial communities, as well as distinguishing and characterizing bacteria-related health conditions.

Background: Vocal features have been proposed as a way to identify depression by distinguishing depression from healthy controls, but while there have been some claims for success, the degree to which changes in vocal features are specific to depression has not been systematically studied. In particular, it is not clear whether vocal features are characteristic of mental ill health in general, rather than characteristic of different psychiatric diagnoses. We examined the performance of vocal features in recognizing three diseases (depression, schizophrenia and bipolar disorder) in comparison with healthy controls and in pairwise comparison with each disease in turn.Methods: We sampled 32 bipolar disorder patients; 106 depression patients, 114 healthy controls and 20 schizophrenia patients. We extracted i-vectors from MFCCs features, and built logistic regression models with ridge regularization and 5 fold cross validation on the training set, then applied models to the test set.Results: Our results showed that AUC score for classifying depression and bipolar disorder is 0.5 (F-score = 0.44). For other comparisons, AUC scores range from 0.75 to 0.92 (F-score ranges from 0.73 to 0.91). The performance (AUC) of depression and bipolar disorder classification model is significantly worse than the performance of bipolar disorder and schizophrenia classification model (corrected P < 0.05). We found no significant difference in pairwise ROC difference tests among the remaining classification tasks.Conclusions: Vocal features have robust discriminatory power not only in classifying depression and health, but also in pairwise classification among different mental illness.

The translational power of human microbiome studies is limited by high inter-individual variation. We describe a dimensionality reduction tool, compositional tensor factorization (CTF), that incorporates information from the same host, across multiple samples, to reveal patterns driving differences in microbial composition across phenotypes. CTF identifies robust patterns in sparse, compositional datasets, allowing for the detection of microbial changes associated with specific phenotypes that are reproducible across datasets.

Dynamic changes in microbial communities play an important role in human health and disease. Specifically, deciphering how microbial species in a community interact with each other and their environment can elucidate mechanisms of disease, a problem typically investigated using tools from community ecology. Yet, such methods require measurements of absolute densities, whereas typical datasets only provide estimates of relative abundances. Here, we systematically investigate models of microbial dynamics in the simplex of relative abundances. We derive a new nonlinear dynamical system for microbial dynamics, termed “compositional” Lotka-Volterra (cLV), unifying approaches using generalized Lotka-Volterra (gLV) equations from community ecology and compositional data analysis. On three real datasets, we demonstrate that cLV recapitulates interactions between relative abundances implied by gLV. Moreover, we show that cLV is as accurate as gLV in forecasting microbial trajectories in terms of relative abundances. We further compare cLV to two other models of relative abundance dynamics motivated by common assumptions in the literature—a linear model in a log-ratio transformed space, and a linear model in the space of relative abundances—and provide evidence that cLV more accurately describes community trajectories over time. Finally, we investigate when information about direct effects can be recovered from relative data that naively provide information about only indirect effects. Our results suggest that strong effects may be recoverable from relative data, but more subtle effects are challenging to identify., Author summary Dynamic changes in microbial communities play an important role in human health and disease. Specifically, deciphering how microbial species in a community interact with each other and their environment can elucidate mechanisms of disease, a problem typically investigated using tools from community ecology. Yet, such methods require measurements of absolute densities, whereas typical only provide estimates of relative abundances. We investigate methods for describing microbial dynamics in terms of relative abundances using approaches from machine learning and dynamical systems. Across three real datasets, we show that relative abundances are sufficient to describe compositional dynamics. Additionally, we show that models trained on relative abundances alone predict future compositions as well models trained on absolute abundances. Finally, we provide criteria for when direct effects, which typically can only be learned from absolute abundances, are recoverable for relative data. As a proof of concept, we recapitulate a previously proposed interaction network for C. difficile colonization.

How complex communities assemble through the animal’s life, and how predictable the process is remains unexplored. Here, we investigate the forces that drive the assembly of rumen microbiomes throughout a cow’s life, with emphasis on the balance between stochastic and deterministic processes. We analyse the development of the rumen microbiome from birth to adulthood using 16S-rRNA amplicon sequencing data and find that the animals shared a group of core successional species that invaded early on and persisted until adulthood. Along with deterministic factors, such as age and diet, early arriving species exerted strong priority effects, whereby dynamics of late successional taxa were strongly dependent on microbiome composition at early life stages. Priority effects also manifest as dramatic changes in microbiome development dynamics between animals delivered by C-section vs. natural birth, with the former undergoing much more rapid species invasion and accelerated microbiome development. Overall, our findings show that together with strong deterministic constrains imposed by diet and age, stochastic colonization in early life has long-lasting impacts on the development of animal microbiomes., How complex microbial communities assemble through the animal’s life, and how predictable the assembly process is, remains poorly understood. Here, the authors profile the cow gut microbiome from birth to adulthood in animals born in C-section or natural birth and show that chance events early in life have a strong impact on microbiome development.

We introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell-type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer cell counts without methylation reference only capture linear combinations of cell counts rather than provide one component per cell type. Our approach allows the construction of components such that each component corresponds to a single cell type, and provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before.

Estimation of heritability is an important task in genetics. The use of linear mixed models (LMMs) to determine narrow-sense single-nucleotide polymorphism (SNP)-heritability and related quantities has received much recent attention, due of its ability to account for variants with small effect sizes. Typically, heritability estimation under LMMs uses the restricted maximum likelihood (REML) approach. The common way to report the uncertainty in REML estimation uses standard errors (SEs), which rely on asymptotic properties. However, these assumptions are often violated because of the bounded parameter space, statistical dependencies, and limited sample size, leading to biased estimates and inflated or deflated confidence intervals (CIs). In addition, for larger data sets (e.g., tens of thousands of individuals), the construction of SEs itself may require considerable time, as it requires expensive matrix inversions and multiplications. Here, we present FIESTA (Fast confidence IntErvals using STochastic Approximation), a method for constructing accurate CIs. FIESTA is based on parametric bootstrap sampling, and, therefore, avoids unjustified assumptions on the distribution of the heritability estimator. FIESTA uses stochastic approximation techniques, which accelerate the construction of CIs by several orders of magnitude, compared with previous approaches as well as to the analytical approximation used by SEs. FIESTA builds accurate CIs rapidly, for example, requiring only several seconds for data sets of tens of thousands of individuals, making FIESTA a very fast solution to the problem of building accurate CIs for heritability for all data set sizes.

Maximizing elements for your genome The accumulation of mutations is typically limited by selective parameters. One such parameter is the elements needed to build proteins and molecules to maintain cells. Examining the underlying carbon, oxygen, and nitrogen content for different amino acids, Shenhav et al. examined the selective pressure resulting from nutrient limitation (see the Perspective by Polz and Cordero). The authors identified “resource-driven” selection as a purifying selective force associated with environmental nutrient availability, particularly nitrogen, and determined the impact of mutations on the organismal nutritional budget. From this constraint, the authors have proposed that the structure of the genetic code across organisms reflects the mutational impact on elemental resources. Science , this issue p. 683 ; see also p. 655

Given the highly dynamic and complex nature of the human gut microbial community, the ability to identify and predict time-dependent compositional patterns of microbes is crucial to our understanding of the structure and functions of this ecosystem. One factor that could affect such time-dependent patterns is microbial interactions, wherein community composition at a given time point affects the microbial composition at a later time point. However, the field has not yet settled on the degree of this effect. Specifically, it has been recently suggested that only a minority of taxa depend on the microbial composition in earlier times. To address the issue of identifying and predicting temporal microbial patterns we developed a new model, MTV-LMM (Microbial Temporal Variability Linear Mixed Model), a linear mixed model for the prediction of microbial community temporal dynamics. MTV-LMM can identify time-dependent microbes (i.e., microbes whose abundance can be predicted based on the previous microbial composition) in longitudinal studies, which can then be used to analyze the trajectory of the microbiome over time. We evaluated the performance of MTV-LMM on real and synthetic time series datasets, and found that MTV-LMM outperforms commonly used methods for microbiome time series modeling. Particularly, we demonstrate that the effect of the microbial composition in previous time points on the abundance of taxa at later time points is underestimated by a factor of at least 10 when applying previous approaches. Using MTV-LMM, we demonstrate that a considerable portion of the human gut microbiome, both in infants and adults, has a significant time-dependent component that can be predicted based on microbiome composition in earlier time points. This suggests that microbiome composition at a given time point is a major factor in defining future microbiome composition and that this phenomenon is considerably more common than previously reported for the human gut microbiome., Author summary The ability to characterize and predict temporal trajectories of the microbial community in the human gut is crucial to our understanding of the structure and functions of this ecosystem. In this study we develop MTV-LMM, a method for modeling time-series microbial community data. Using MTV-LMM we find that in contrast to previous reports, a considerable portion of microbial taxa in both infants and adults display temporal structure that is predictable using the previous composition of the microbial community. In reaching this conclusion we have adopted a number of concepts common in statistical genetics for use with longitudinal microbiome studies. We introduce concepts such as time-explainability and the temporal kinship matrix, which we believe will be of use to other researchers studying microbial dynamics, through the framework of linear mixed models. In particular we find that the association matrix estimated by MTV-LMM reveals known phylogenetic relationships and that the temporal kinship matrix uncovers known temporal structure in infant microbiome and inter-individual differences in adult microbiome. Finally, we demonstrate that MTV-LMM significantly outperforms commonly used methods for temporal modeling of the microbiome, both in terms of its prediction accuracy as well as in its ability to identify time-dependent taxa.

Systematic reviews and meta-analyses are important tools for synthesizing evidence from multiple studies. They serve to increase power and improve precision, in the same way that large studies can do, but also to establish the consistency of effects and replicability of results across studies. In this work we propose statistical tools to quantify replicability of effect signs (or directions) and their consistency. We suggest that these tools accompany the fixed-effect or random-effects meta-analysis, and we show that they convey important information for the assessment of the intervention under investigation. We motivate and demonstrate our approach and its implications by examples from systematic reviews from the Cochrane Library. Our tools make no assumptions on the distribution of the true effect sizes, so their inferential guarantees continue to hold even if the assumptions of the fixed-effect or random-effects models do not hold. We also develop a version of this tool under the fixed-effect assumption for cases where it is crucial and justified.

Purpose To examine the efficacy of corneal collagen cross-linking (CXL) for the treatment of keratoconus (KCN). Methods A systemic literature review and meta-analysis of ocular functional and structural parameters of patients with KCN undergoing cross-linking procedures were performed using PubMed and the web of science. A literature search was performed for relevant peer-reviewed publications on population-based studies. Data were analyzed with R software (Meta library), and heterogeneity was assessed with the Cochran Q and I. A random-effects model was used for high heterogeneity; otherwise a fixed model was used. Sensitivity analysis of particular tested groups was used to explain high heterogeneity. The main outcome measures extracted from the articles were corrected distance visual acuity, uncorrected distance visual acuity, and maximum K. Results An improvement in visual acuity of 1 to 2 Snellen lines was found 3 months or more after undergoing CXL. Changes were more pronounced in uncorrected visual acuity. Some topography parameters were found to be improved (0.6-1 diopters) 12 to 24 months after CXL. The refractive cylinder improved by 0.4 to 0.7 diopters. Endothelial cell density decreased by 225 cells per square millimeter in the first 3 months and thereafter returned to normal. Corneal thickness was reduced by 10 to 20 μm in the year following CXL but not after 24 months. No changes in intraocular pressure were noted. Conclusions CXL is a safe and effective method for halting the deterioration of KCN, while slightly improving visual function.

Summary GLINT is a user-friendly command-line toolset for fast analysis of genome-wide DNA methylation data generated using the Illumina human methylation arrays. GLINT, which does not require any programming proficiency, allows an easy execution of Epigenome-Wide Association Study analysis pipeline under different models while accounting for known confounders in methylation data. Availability and Implementation GLINT is a command-line software, freely available at https://github.com/cozygene/glint/releases. It requires Python 2.7 and several freely available Python packages. Further information and documentation as well as a quick start tutorial are available at http://glint-epigenetics.readthedocs.io.

A large proportion of Depression Disorder patients do not receive an effective diagnosis, which makes it necessary to find a more objective assessment to facilitate a more rapid and accurate diagnosis of depression. Speech data is easy to acquire clinically, its association with depression has been studied, although the actual predictive effect of voice features has not been examined. Thus, we do not have a general understanding of the extent to which voice features contribute to the identification of depression. In this study, we investigated the significance of the association between voice features and depression using binary logistic regression, and the actual classification effect of voice features on depression was re-examined through classification modeling. Nearly 1000 Chinese females participated in this study. Several different datasets was included as test set. We found that 4 voice features (PC1, PC6, PC17, PC24, P

Longitudinal studies of microbial communities have emphasized that host-associated microbiota are highly dynamic as well as underscoring the potential biomedical relevance of understanding these dynamics. Despite this increasing appreciation, statistical challenges in the design and analysis of longitudinal microbiome studies such as sequence counting, technical variation, signal aliasing, contamination, sparsity, missing data, and algorithmic scalability remain. In this review we discuss these challenges and highlight current progress in the field. Where possible, we try to provide guidelines for best practices as well as discuss how to tailor design and analysis to the hypothesis and ecosystem under study. Overall, this review is intended to serve as an introduction to longitudinal microbiome studies for both statisticians new to the microbiome field as well as biologists with little prior experience with longitudinal study design and analysis.

Given the highly dynamic and complex nature of the human gut microbial community, the ability to identify and predict time-dependent compositional patterns of microbes is crucial to our understanding of the structure and function of this ecosystem. One factor that could affect such time-dependent patterns is microbial interactions, wherein community composition at a given time point affects the microbial composition at a later time point. However, the field has not yet settled on the degree of this effect. Specifically, it has been recently suggested that only a minority of the operational taxonomic units (OTUs) depend on the microbial composition in earlier times. To address the issue of identifying and predicting temporal microbial patterns we developed a new model, MTV-LMM (Microbial Temporal Variability Linear Mixed Model), a linear mixed model for the prediction of the microbial community temporal dynamics based on the community composition at previous time stamps. MTV-LMM can identify time-dependent microbes in time series datasets, which can then be used to analyze the trajectory of the microbiome over time. We evaluated the performance of MTV-LMM on three human microbiome time series datasets, and found that MTV-LMM significantly outperforms all existing methods for microbiome time series modeling. Particularly, we demonstrate that the effect of the microbial composition in previous time points on the abundance levels of an OTU at a later time point is underestimated by a factor of at least 10 when applying previous approaches. Using MTV-LMM, we demonstrate that a considerable proportion of the human gut microbiome, both in infants and adults, has a significant time-dependent component that can be predicted based on microbiome composition in earlier time points. This suggests that microbiome composition at a given time point is a major factor in defining future microbiome composition and that this phenomenon is considerably more common than previously reported for the human gut microbiome.

Genome-wide DNA methylation levels measured from a target tissue across a population have become ubiquitous over the last few years, as methylation status is suggested to hold great potential for better understanding the role of epigenetics. Different cell types are known to have different methylation profiles. Therefore, in the common scenario where methylation levels are collected from heterogeneous sources such as blood, convoluted signals are formed according to the cell type composition of the samples. Knowledge of the cell type proportions is important for statistical analysis, and it may provide novel biological insights and contribute to our understanding of disease biology. Since high resolution cell counting is costly and often logistically impractical to obtain in large studies, targeted methods that are inexpensive and practical for estimating cell proportions are needed. Although a supervised approach has been shown to provide reasonable estimates of cell proportions, this approach leverages scarce reference methylation data from sorted cells which are not available for most tissues and are not appropriate for any target population. Here, we introduce BayesCCE, a Bayesian semi-supervised method that leverages prior knowledge on the cell type composition distribution in the studied tissue. As we demonstrate, such prior information is substantially easier to obtain compared to appropriate reference methylation levels from sorted cells. Using real and simulated data, we show that our proposed method is able to construct a set of components, each corresponding to a single cell type, and together providing up to 50% improvement in correlation when compared with existing reference-free methods. We further make a design suggestion for future data collection efforts by showing that results can be further improved using cell count measurements for a small subset of individuals in the study sample or by incorporating external data of individuals with measured cell counts. Our approach provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before.

Background Genetic data are known to harbor information about human demographics, and genotyping data are commonly used for capturing ancestry information by leveraging genome-wide differences between populations. In contrast, it is not clear to what extent population structure is captured by whole-genome DNA methylation data. Results We demonstrate, using three large-cohort 450K methylation array data sets, that ancestry information signal is mirrored in genome-wide DNA methylation data and that it can be further isolated more effectively by leveraging the correlation structure of CpGs with cis-located SNPs. Based on these insights, we propose a method, EPISTRUCTURE, for the inference of ancestry from methylation data, without the need for genotype data. Conclusions EPISTRUCTURE can be used to infer ancestry information of individuals based on their methylation data in the absence of corresponding genetic data. Although genetic data are often collected in epigenetic studies of large cohorts, these are typically not made publicly available, making the application of EPISTRUCTURE especially useful for anyone working on public data. Implementation of EPISTRUCTURE is available in GLINT, our recently released toolset for DNA methylation analysis at: http://glint-epigenetics.readthedocs.io. Electronic supplementary material The online version of this article (doi:10.1186/s13072-016-0108-y) contains supplementary material, which is available to authorized users.

Estimation of heritability is an important task in genetics. The use of linear mixed models (LMMs) to determine narrow-sense SNP-heritability and related quantities has received much recent attention, due of its ability to account for variants with small effect sizes. Typically, heritability estimation under LMMs uses the restricted maximum likelihood (REML) approach. The common way to report the uncertainty in REML estimation uses standard errors (SE), which rely on asymptotic properties. However, these assumptions are often violated because of the bounded parameter space, statistical dependencies, and limited sample size, leading to biased estimates and inflated or deflated confidence intervals. In addition, for larger datasets (e.g., tens of thousands of individuals), the construction of SEs itself may require considerable time, as it requires expensive matrix inversions and multiplications.