Motivated by the analysis of torsion (dihedral) angles in the backbone of proteins, we investigate clustering of bivariate angular data on the torus $[-\mathit{\pi },\mathit{\pi })\times [-\mathit{\pi },\mathit{\pi })$. We show that naive adaptations of clustering methods, designed for vector-valued data, to the torus are not satisfactory and propose a novel clustering approach based on the conformal prediction framework. We construct several prediction sets for toroidal data with guaranteed finite-sample validity, based on a kernel density estimate and bivariate von Mises mixture models. From a prediction set built from a Gaussian approximation of the bivariate von Mises mixture, we propose a data-driven choice for the number of clusters and present algorithms for an automated cluster identification and cluster membership assignment. The proposed prediction sets and clustering approaches are applied to the torsion angles extracted from three strains of coronavirus spike glycoproteins (including SARS-CoV-2, contagious in humans). The analysis reveals a potential difference in the clusters of the SARS-CoV-2 torsion angles, compared to the clusters found in torsion angles from two different strains of coronavirus, contagious in animals.

In this paper, a fast and parallelizable method based on Gaussian processes (GPs) is introduced to emulate computer models that simulate the formation of binary black holes (BBHs) through the evolution of pairs of massive stars. Two obstacles that arise in this application are the a priori unknown conditions of BBH formation and the large scale of the simulation data. We address them by proposing a local emulator which combines a GP classifier and a GP regression model. The resulting emulator can also be utilized in planning future computer simulations through a proposed criterion for sequential design. By propagating uncertainties of simulation input through the emulator, we are able to obtain the distribution of BBH properties under the distribution of physical parameters.

In agricultural landscapes the spatial distribution of cultivated and seminatural elements strongly impacts habitat connectivity and species dynamics. To allow for landscape structural analysis and scenario generation, we here develop statistical tools for real landscapes composed of geometric elements, including 2D patches but also 1D linear elements (e.g., hedges). Utilizing the framework of discrete Markov random fields, we design generative stochastic models that combine a multiplex network representation, based on spatial adjacency, with Gibbs energy terms to capture the distribution of landscape descriptors for land-use categories. We implement simulation of agricultural scenarios with parameter-controlled spatial and temporal patterns (e.g., geometry, connectivity, crop rotation), and we demonstrate through simulation that pseudo-likelihood estimation of parameters works well. To study statistical relevance of model components in real landscapes, we discuss model selection and validation, including cross-validated prediction scores. Model validation with a view toward ecologically relevant landscape summaries is achieved by comparing observed and simulated summaries (network metrics but also metrics and appropriately defined variograms using a raster discretization). Models fitted to subregions of the Lower Durance Valley (France) indicate strong deviation from random allocation and realistically capture landscape patterns. In summary, our approach improves the understanding of agroecosystems and enables simulation-based theoretical analysis of how landscape patterns shape biological and ecological processes.

Single nucleotide polymorphism (SNP) set analysis aggregates both common and rare variants and tests for association between phenotype(s) of interest and a set. However, multiple SNP-sets, such as genes, pathways, or sliding windows are usually investigated across the whole genome in which all groups are tested separately, followed by multiple testing adjustments. We propose a novel method to prioritize SNP-sets in a joint multivariate variance component model. Each SNP-set corresponds to a variance component (or kernel), and model selection is achieved by incorporating either convex or nonconvex penalties. The uniqueness of this variance component selection framework, which we call VCSEL, is that it naturally encompasses multivariate traits (VCSEL-M) and SNP-set-treatment or -environment interactions (VCSEL-I). We devise an optimization algorithm scalable to many variance components, based on the majorization-minimization (MM) principle. Simulation studies demonstrate the superiority of our methods in model selection performance, as measured by the area under the precision-recall (PR) curve, compared to the commonly used marginal testing and group penalization methods. Finally, we apply our methods to a real pharmacogenomics study and a real whole exome sequencing study. Some top ranked genes by VCSEL are detected as insignificant by the marginal test methods which emphasizes formal inference of individual genes with a strict significance threshold. This provides alternative insights for biologists to prioritize follow-up studies and develop polygenic risk score models.

Differential gene expression analysis based on scRNA-seq data is challenging due to two unique characteristics of scRNA-seq data. First, multimodality and other heterogeneity of the gene expression among different cell conditions lead to divergences in the tail events or crossings of the expression distributions. Second, scRNA-seq data generally have a considerable fraction of dropout events, causing zero inflation in the expression. To account for the first characteristic, existing parametric approaches targeting the mean difference in gene expression are limited, while quantile regression that examines various locations in the distribution will improve the power. However, the second characteristic, zero inflation, makes the traditional quantile regression invalid and underpowered. We propose a quantile-based test that handles the two characteristics, multimodality and zero inflation, simultaneously. The proposed quantile rank-score based test for differential distribution detection (ZIQRank) is derived under a two-part quantile regression model for zero-inflated outcomes. It comprises a test in logistic modeling for the zero counts and a collection of rank-score tests adjusting for zero inflation at multiple prespecified quantiles of the positive part. The testing decision is based on an aggregate result by combining the marginal p-values by MinP or Cauchy procedure. The proposed test is asymptotically justified and evaluated with simulation studies. It shows a higher precision-recall AUC in detecting true differentially expressed genes (DEGs) than the existing methods. We apply the ZIQRank test to a TPM scRNA-seq data on human glioblastoma tumors and exclusively identify a group of DEGs between neoplastic and nonneoplastic cells, which are heterogeneous and have been proved to be associated with glioma. Application to a UMI count scRNA-seq data on cells from mouse intestinal organoids further demonstrates the capability of ZIQRank to improve and complement the existing approaches.

The two main approaches in the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genomics. While both histopathology and genomics are fundamental to cancer research, the connections between these fields have been relatively superficial. We bridge this gap by investigating the Carolina Breast Cancer Study through the development of an integrative, exploratory analysis framework. Our analysis gives insights—some known, some novel—that are engaging to both pathologists and geneticists. Our analysis framework is based on angle-based joint and individual variation explained (AJIVE) for statistical data integration and exploits convolutional neural networks (CNNs) as a powerful, automatic method for image feature extraction. CNNs raise interpretability issues that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g., PCA or AJIVE) applied to CNN features.

This paper analyzes breast cancer gene expression across seven studies to identify genuine and thus replicable gene patterns shared among these studies. Our premise is that genuine biological signal is more likely to be reproducibly present in multiple studies than spurious signal. Our analysis uses a new modeling strategy for the joint analysis of high-throughput biological studies which simultaneously identifies shared as well as study-specific signal. To this end, we generalize the multi-study factor analysis model to handle high-dimensional data and generalize the sparse Bayesian infinite factor model to this context. We provide strategies for the identification of the loading matrices, common and study-specific. Through extensive simulation analysis, we characterize the performance of the proposed approach in various scenarios and show that it outperforms standard factor analysis in identifying replicable signal in all scenarios considered. The analysis of breast cancer gene expression studies identifies clear replicable gene patterns. These patterns are related to well-known biological pathways involved in breast cancer, such as the ER, cell cycle, immune system, collagen, and metabolic pathways. Some of these patterns are also associated with existing breast cancer subtypes, such as LumA, Her2, and basal subtypes, while other patterns identify novel pathways active across subtypes and missed by hierarchical clustering approaches. The R package MSFA implementing the method is available on GitHub.

We consider the problem of single-molecule identification in super-resolution microscopy. Super-resolution microscopy overcomes the diffraction limit by localizing individual fluorescing molecules in a field of view. This is particularly difficult since each individual molecule appears and disappears randomly across time and because the total number of molecules in the field of view is unknown. Additionally, data sets acquired with super-resolution microscopes can contain a large number of spurious fluorescent fluctuations caused by background noise.

To address these problems, we present a Bayesian nonparametric framework capable of identifying individual emitting molecules in super-resolved time series. We tackle the localization problem in the case in which each individual molecule is already localized in space. First, we collapse observations in time and develop a fast algorithm that builds upon the Dirichlet process. Next, we augment the model to account for the temporal aspect of fluorophore photophysics. Finally, we assess the performance of our methods with ground-truth data sets having known biological structure.

Network meta-analysis (NMA) is a powerful tool to compare multiple treatments directly and indirectly by combining and contrasting multiple independent clinical trials. Because many NMAs collect only a few eligible randomized controlled trials (RCTs), there is an urgent need to synthesize different sources of information, for example, from both RCTs and single-arm trials. However, single-arm trials and RCTs may have different populations and quality so that assuming they are exchangeable may be inappropriate. This article presents a novel method using a commensurate prior on variance (CPV) to borrow variance (rather than mean) information from single-arm trials in an arm-based (AB) Bayesian NMA. We illustrate the advantages of this CPV method by reanalyzing an NMA of immune checkpoint inhibitors in cancer patients. Comprehensive simulations investigate the impact on statistical inference of including single-arm trials. The simulation results show that the CPV method provides efficient and robust estimation, even when the two sources of information are moderately inconsistent.

The human gastrointestinal tract is an environment that hosts an ecosystem of microorganisms essential to human health. Vital biological processes emerge from fundamental inter- and intraspecies molecular interactions that influence the assembly and composition of the gut microbiota ecology. Here, we quantify the complexity of the ecological relationships within the human infant gut microbiota ecosystem as a function of the information contained in the nonlinear associations of a sequence of increasingly specified maximum entropy representations of the system. Our paradigm frames the ecological state, in terms of the presence or absence of individual microbial ecological units that are identified by amplicon sequence variants (ASV) in the gut microenvironment, as a function of both the ecological states of its neighboring units and, in a departure from standard graphical model representations, the associations among the units within its neighborhood. We characterize the order of the system based on the relative quantity of statistical information encoded by high-order statistical associations of the infant gut microbiota.

Recent technological advancements have enabled detailed investigation of associations between the molecular architecture and tumor heterogeneity through multisource integration of radiological imaging and genomic (radiogenomic) data. In this paper we integrate and harness radiogenomic data in patients with lower grade gliomas (LGG), a type of brain cancer, in order to develop a regression framework called RADIOHEAD (RADIOgenomic analysis incorporating tumor HEterogeneity in imAging through Densities) to identify radiogenomic associations. Imaging data is represented through voxel-intensity probability density functions of tumor subregions obtained from multimodal magnetic resonance imaging and genomic data through molecular signatures in the form of pathway enrichment scores corresponding to their gene expression profiles. Employing a Riemannian-geometric framework for principal component analysis on the set of probability density functions, we map each probability density to a vector of principal component scores which are then included as predictors in a Bayesian regression model with the pathway enrichment scores as the response. Variable selection compatible with the grouping structure amongst the predictors induced through the tumor subregions is carried out under a group spike-and-slab prior. A Bayesian false discovery rate mechanism is then used to infer significant associations based on the posterior distribution of the regression coefficients. Our analyses reveal several pathways relevant to LGG etiology (such as synaptic transmission, nerve impulse and neurotransmitter pathways) to have significant associations with the corresponding imaging-based predictors.

Facing increasing societal and economic pressure, many countries have established strategies to develop renewable energy portfolios whose penetration in the market can alleviate the dependence on fossil fuels. In the case of wind, there is a fundamental question related to the resilience and hence profitability of future wind farms to a changing climate, given that current wind turbines have lifespans of up to 30 years. In this work we develop a new non-Gaussian method to adjust assimilated observational data to simulations and to estimate future wind, predicated on a trans-Gaussian transformation and a clusterwise minimization of the Kullback–Leibler divergence. Future winds abundance will be determined for Saudi Arabia, a country with a recently established plan to develop a portfolio of up to 16 GW of wind energy. Further, we estimate the change in profits over future decades using additional high-resolution simulations, an improved method for vertical wind extrapolation and power curves from a collection of popular wind turbines. We find an overall increase in daily profit of $272,000 for the wind energy market for the optimal locations for wind farming in the country.

Using aeroelastic stochastic simulations, this study presents an importance sampling method for assessing wind turbine reliability. As the size of modern wind turbines gets larger, structural reliability analysis becomes more important to prevent any catastrophic failures. At the design stage, operational data do not exist or are scarce. Therefore, aeroelastic simulation is often employed for reliability analysis. Importance sampling is one of the powerful variance reduction techniques to mitigate computational burden in stochastic simulations. In the literature, wind turbine reliability assessment with importance sampling has been studied with a single variable, wind speed. However, other atmospheric stability conditions also impose substantial stress on the turbine structure. Moreover, each environmental factor’s effect on the turbine’s load response depends on other factors. This study investigates how multiple environmental factors collectively affect the turbine reliability. Specifically, we devise a new nonparametric importance sampling method that can quantify the contributions of each environmental factor and its interactions with other factors, while avoiding computational problems and data sparsity issue arising in rare event simulation. Our wind turbine case study and numerical examples demonstrate the advantage of the proposed approach.

Models of the spatial distribution of animals provide useful tools to help ecologists quantify species-environment relationships, and they are increasingly being used to help determine the impacts of climate and habitat changes on species. While high-quality survey-style data with known effort are sometimes available, often researchers have multiple datasets of varying quality and type. In particular, collections of sightings made by citizen scientists are becoming increasingly common, with no information typically provided on their observer effort. Many standard modelling approaches ignore observer effort completely which can severely bias estimates of an animal’s distribution. Combining sightings data from observers who followed different protocols is challenging. Any differences in observer skill, spatial effort and the detectability of the animals across space all need to be accounted for. To achieve this, we build upon the recent advancements made in integrative species distribution models and present a novel marked spatiotemporal point process framework for estimating the utilization distribution (UD) of the individuals of a highly mobile species. We show that, in certain settings, we can also use the framework to combine the UDs from the sampled individuals to estimate the species’ distribution. We combine the empirical results from a simulation study with the implications outlined in a causal directed acyclic graph to identify the necessary assumptions required for our framework to control for observer effort when it is unknown. We then apply our framework to combine multiple datasets collected on the endangered Southern Resident Killer Whales to estimate their monthly effort-corrected space-use.

The use of spatial models for inferring members’ preferences from voting data has become widespread in the study of deliberative bodies, such as legislatures. Most established spatial voting models assume that ideal points belong to a Euclidean policy space. However, the geometry of Euclidean spaces (even multidimensional ones) cannot fully accommodate situations in which members at the opposite ends of the ideological spectrum reveal similar preferences by voting together against the rest of the legislature. This kind of voting behavior can arise, for example, when extreme conservatives oppose a measure because they see it as being too costly, while extreme liberals oppose it for not going far enough for them. This paper introduces a new class of spatial voting models in which preferences live in a circular policy space. Such geometry for the latent space is motivated by both theoretical (the so-called “horseshoe theory” of political thinking) and empirical (goodness of fit) considerations. Furthermore, the circular model is flexible and can approximate the one-dimensional version of the Euclidean voting model when the data supports it. We apply our circular model to roll-call voting data from the U.S. Congress between 1988 and 2019 and demonstrate that, starting with the 112th House of Representatives, circular policy spaces consistently provide a better explanation of legislators’s behavior than Euclidean ones and that legislators’s rankings, generated through the use of the circular geometry, tend to be more consistent with those implied by their stated policy positions.

Dáil Éireann is the principal chamber of the Irish parliament. The 31st Dáil was in session from March 11th, 2011 to February 6th, 2016. Many of the members of the Dáil were active on social media, and many were Twitter users who followed other members of the Dáil. The pattern of Twitter following amongst these politicians provides insights into political alignment within the Dáil. We propose a new model, called the generalized latent space stochastic blockmodel, which extends and generalizes both the latent space model and the stochastic blockmodel to study social media connections between members of the Dáil. The probability of an edge between two nodes in a network depends on their respective class labels, as well as sender and receiver effects and latent positions in an unobserved latent space. The proposed model is capable of representing transitivity and clustering, as well as disassortative mixing. A Bayesian method with Markov chain Monte Carlo sampling is proposed for estimation of model parameters. Model selection is performed using the WAIC criterion and models of different number of classes or dimensions of latent space are compared. We use the model to study Twitter following relationships of members of the Dáil and interpret structure found in these relationships. We find that the following relationships amongst politicians is mainly driven by past and present political party membership. We also find that the modeling outputs are informative when studying voting within the Dáil.

Prevalence is of essential importance in biomedical and public health research. In the “classic” paradigm it has been studied for each disease individually. Accumulating evidence has shown that diseases can be “correlated.” Joint analysis of prevalence can potentially provide important insights beyond individual-disease analysis but has not been well pursued. In this study we take advantage of the unique Taiwan National Health Insurance Research Database (NHIRD) and conduct the first pan-disease analysis of period prevalence trend. The goal is to identify clusters within which diseases have similar period prevalence trends. A novel penalization pursuit approach is applied which has an intuitive formulation and preferable numerical performance. In data analysis the period prevalence values are computed using the records on close to one million subjects and 14 years of observation. With 405 diseases, 35 clusters with sizes larger than one and 27 clusters with sizes one are identified. The clustering results have sound interpretations and differ significantly from those of the alternatives.

Despite substantial advances in global measles vaccination, measles disease burden remains high in many low- and middle-income countries. A key public health strategy for controlling measles in such high-burden settings is to conduct supplementary immunization activities (SIAs) in the form of mass vaccination campaigns, in addition to delivering scheduled vaccination through routine immunization (RI) programs. To achieve balanced implementations of RI and SIAs, robust measurement of subnational RI-specific coverage is crucial. In this paper we develop a space–time smoothing model for estimating RI-specific coverage of the first dose of measles-containing-vaccines (MCV1) at subnational level using complex survey data. The application that motivated this work is estimation of the RI-specific MCV1 coverage in Nigeria’s 36 states and the Federal Capital Territory. Data come from four demographic and health surveys, three multiple indicator cluster surveys and two national nutrition and health surveys conducted in Nigeria between 2003 and 2018. Our method incorporates information from the SIA calendar published by the World Health Organization and accounts for the impact of SIAs on the overall MCV1 coverage, as measured by cross-sectional surveys. The model can be used to analyze data from multiple surveys with different data collection schemes and construct coverage estimates with uncertainty that reflects the various sampling designs. Implementation of our method can be done efficiently using integrated nested Laplace approximation (INLA).

The Adaptive Multiple Importance Sampling algorithm (AMIS) is an iterative technique which recycles samples from all previous iterations in order to improve the efficiency of the proposal distribution. We have formulated a new statistical framework, based on AMIS, to take the output from a geostatistical model of infectious disease prevalence, incidence or relative risk, and project it forward in time under a mathematical model for transmission dynamics. We adapted the AMIS algorithm so that it can sample from multiple targets simultaneously by changing the focus of the adaptation at each iteration. By comparing our approach against the standard AMIS algorithm, we showed that these novel adaptations greatly improve the efficiency of the sampling. We tested the performance of our algorithm on four case studies: ascariasis in Ethiopia, onchocerciasis in Togo, human immunodeficiency virus (HIV) in Botswana, and malaria in the Democratic Republic of the Congo.

Transport operators have a range of intervention options available to improve or enhance their networks. Such interventions are often made in the absence of sound evidence on resulting outcomes. Cycling superhighways were promoted as a sustainable and healthy travel mode, one of the aims of which was to reduce traffic congestion. Estimating the impacts that cycle superhighways have on congestion is complicated due to the nonrandom assignment of such intervention over the transport network. In this paper we analyse the causal effect of cycle superhighways utilising preintervention and postintervention information on traffic and road characteristics along with socioeconomic factors. We propose a modeling framework based on the propensity score and outcome regression model. The method is also extended to the doubly robust set-up. Simulation results show the superiority of the performance of the proposed method over existing competitors. The method is applied to analyse a real dataset on the London transport network. The methodology proposed can assist in effective decision making to improve network performance.

A widely noted failure of causal inference occurred when several observational studies claimed that hormone replacement therapy (HRT) reduced risk of cardiovascular disease; yet, subsequent randomized trials found an increased, not a decreased, cardiovascular risk. We take a close look at covariate imbalances in one of the observational data sets. We use some old, some recent, and some new methods, plus we update an important, simple but largely forgotten suggestion of William Cochran about screening covariates and other variables. In particular, a tapered match shows the impact on all covariates of gradually matching for additional covariates. An exterior match examines the change in the control group as additional covariates are included, and the consequences for outcomes. Because covariates are sometimes continuous, sometimes binary, sometimes ordinal, sometimes missing, we suggest keeping track of magnitudes of aggregate bias in observed covariates using a new estimate of the Kullback–Leibler information between covariate distributions in treated and matched control groups, a flexible measure with several attractive properties. The initial studies ignored some enormous imbalances in socioeconomic covariates that predict the outcomes under study. Our more comprehensive analyses mimic some post-game reanalyses done subsequent to the randomized trials; however, even these omit a large imbalance in a consequential covariate discovered by Cochran’s quick but expansive screening suggestion. Our sense is that a closer examination of covariate imbalance would not have led to a correct conclusion about the effects of HRT, but it would have heightened concerns about the magnitude of the problems in the observational studies, and it would have raised doubts about the ability of a few regression coefficients to eliminate all biases, observed and unobserved, in the comparison. Medical journals need to recognize that certain sources of uncertainty cannot be eliminated from certain necessary types of empirical investigation; moreover, these journals need to learn new ways to describe these sources of uncertainty with objectivity and candor.

Metabolomic-based approaches have gained much attention in recent years, due to their promising potential to deliver objective tools for assessment of food intake. In particular, multiple biomarkers have emerged for single foods. However, there is a lack of statistical tools available for combining multiple biomarkers to quantitatively infer food intake. Furthermore, there is a paucity of approaches for estimating the uncertainty around biomarker-based inferred intake.

Here, to estimate the relationship between multiple metabolomic biomarkers and food intake in an intervention study conducted under the A-DIET research programme, a latent variable model, multiMarker, is proposed. The multiMarker model integrates factor analytic and mixture of experts models: the observed biomarker values are related to intake which is described as a continuous latent variable which follows a flexible mixture of experts model with Gaussian components. The multiMarker model also facilitates inference on the latent intake when only biomarker data are subsequently observed. A Bayesian hierarchical modelling framework provides flexibility to adapt to different biomarker distributions and facilitates inference of the latent intake along with its associated uncertainty.

Simulation studies are conducted to assess the performance of the multiMarker model, prior to its application to the motivating application of quantifying apple intake.

We analyse levels and trends of intergenerational social class mobility among three postwar birth cohorts in Britain and examine how much of the observed mobility or immobility in them could be accounted for by existing differences in educational attainment between people from different class backgrounds. We propose for this purpose a method which quantifies associations between categorical variables when we compare groups which differ only in the distribution of a mediating variable, such as education. This is analogous to estimation of indirect effects in causal mediation analysis but is here developed to define and estimate population associations of variables. We propose estimators for these associations which depend only on fitted values from models for the mediator and outcome variables, and propose variance estimators for them. The analysis shows that the part that differences in education play in intergenerational class mobility is by no means so dominant as has been supposed and that, while it varies with gender and with particular mobility transitions, it shows no tendency to change over time.

Predicting the outcome of elections, sporting events, entertainment awards and other competitions has long captured the human imagination. Such prediction is growing in sophistication in these areas, especially in the rapidly growing field of data-driven journalism intended for a general audience as the availability of historical information rapidly balloons. Providing statistical methodology to probabilistically predict competition outcomes faces two main challenges. First, a suitably general modeling approach is necessary to assign probabilities to competitors. Second, the modeling framework must be able to accommodate expert opinion which is usually available but difficult to fully encapsulate in typical data sets. We overcome these challenges with a combined conditional logistic regression/subjective Bayes approach. To illustrate the method, we reanalyze data from a recent Time.com piece in which the authors attempted to predict the 2019 Best Picture Academy Award winner using standard logistic regression. Toward engaging and educating a broad readership, we discuss strategies to deploy the proposed method via an online application.