Multiple sequence alignment (MSA) data play a crucial role in the study of protein mutations, with contact prediction being a notable application. Existing methods are often model-based or algorithmic and typically do not incorporate statistical inference to quantify the uncertainty of the prediction outcomes. To address this, we propose a novel framework that transforms the task of contact prediction into a statistical testing problem. Our approach is motivated by the partial correlation for continuous random variables. With one-hot encoding of MSA data, we are able to construct a partial correlation graph for multivariate categorical variables. In this framework, two connected nodes in the graph indicate that the corresponding positions on the protein form a contact. A new spectrum-based test statistic is introduced to test whether two positions are partially correlated. Moreover, the new framework enables the identification of amino acid combinations that contribute to the correlation within the identified contacts, an important but largely unexplored aspect of protein mutations. Numerical experiments demonstrate that our proposed method is valid in terms of controlling Type I errors and powerful in general. Real data applications on various protein families further validate the practical utility of our approach in coevolution and mutation analysis.

In this paper our objective is to identify the risk factors associated with adolescent marijuana use in Washington State, utilizing data from the 2018 and 2021 Healthy Youth Survey (HYS). Despite the survey’s assurance of anonymity, the possibility of over- or underreporting exists due to various reasons, such as fear of being exposed, social stigma, and peer pressure. We are also interested in identifying factors that are associated with the occurrence of misreport. To achieve these goals, we develop a full Bayesian framework with a two-level latent linear regression model. The top level is for the true marijuana use response, and the second level is for the occurrence of misreporting. An informative prior is designed to seamlessly incorporate the domain knowledge or prior information while minimizing the risk of prior misspecification. We propose a partially collapsed Gibbs sampling algorithm with a Metropolis–Hastings step to sample the regression coefficients. Simulation studies have been conducted to demonstrate the superior performance of the proposed method over alternative approaches. Our analysis of HYS data discovers multiple factors for identifying at-risk adolescents and informing future prevention efforts.

Human disease network (HDN) analysis, which jointly considers a large number of diseases and focuses on their interconnections, is getting increasingly popular and can shed important insight not possessed by individual-disease-based analysis. Multiple network analysis techniques have been developed for HDNs, although new developments are still strongly needed. In this article we adopt latent space modeling, which has proven powerful in other network analysis contexts and offers unique, insightful interpretations, but has been limitedly applied in HDN analysis. Different from some other types of network analysis and some other HDN analyses (such as gene-centric ones), in this article we pay unique attention to modeling temporal variations. For this purpose, a penalization approach is developed, which can identify time regions with constant network structures (that correspond to ignorable changes) as well as those with smooth variations. The statistical and computational properties are rigorously established. With Medicare data—one of the most powerful medical claims databases—we analyze the admission records of 133 million hospital inpatient treatments from January 2008 to December 2019. Sensible findings are made on disease interconnections and clustering structures. Additionally, the temporal variations, which have not been revealed in the literature, are found to be interpretable. The analysis can provide a new way for connecting and grouping diseases and assist in understanding and planning medical resources.

T cells play a vital role in the immune system by identifying and eliminating infected or tumor cells through the recognition of antigen peptides presented by MHC. The strength of this immune response is directly influenced by the affinity of the T-cell receptor (TCR) for antigen peptides. Recent advancements in high-throughput sequencing technology have enabled the generation of large amounts of paired sequence data comprising TCRs and their corresponding bound antigen peptides. Although various deep-learning methodologies have been proposed to predict TCR-peptide binding, several challenges remain unsolved, including the absence of negative data, the difficulty in modeling the noncontributory residues, and the lack of model validation strategies. To address these challenges, we have developed a probabilistic generative model, termed the Sequence Interaction Model (SIM). This model is designed to predict sequence interactions and has been applied to TCR-peptide binding. SIM is trained solely on positive data sets and offers several advantages: it elucidates the binding preference through an interaction score matrix, generates peptides that bind specifically to a collection of TCRs and distinguishes between noncontributory residues and those essential for binding. We validated our model on the simulated dataset, paired sequence data and experimental structural data.

Identifying genes that display spatial patterns is critical to investigating expression interactions within a spatial context and further dissecting biological understanding of complex mechanistic functionality. Despite the increase in statistical methods designed to identify spatially variable genes, they are mostly based on marginal analysis and share the limitation that the dependence (network) structures among genes are not well accommodated, where a biological process usually involves changes in multiple genes that interact in a complex network. In addition, the latent cellular composition within the spots can introduce confounding variations, negatively affecting the accuracy of the identification. In this study we develop a novel Bayesian regularization approach for spatial transcriptomic data, with confounding variations induced by varying cellular distributions effectively corrected. Significantly advancing from existing studies, a thresholded graph Laplacian regularization is proposed to simultaneously identify spatially variable genes and accommodate the network structure among genes. The proposed method is based on a zero-inflated negative binomial distribution, effectively accommodating the count nature, zero inflation, and overdispersion of spatial transcriptomic data. Extensive simulations and applications to real data demonstrate the competitive performance of the proposed method.

Major depressive disorder (MDD), a leading cause of disability and mortality, is associated with reward-processing abnormalities and concentration issues. Motivated by the probabilistic reward task from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, we propose a novel framework that integrates the reinforcement learning (RL) model and drift-diffusion model (DDM) to jointly analyze reward-based decision-making with response times. To account for emerging evidence suggesting that decision-making may alternate between multiple interleaved strategies, we model latent state switching using a hidden Markov model (HMM). In the engaged state, decisions follow an RL-DDM, simultaneously capturing reward processing, decision dynamics, and temporal structure. In contrast, in the lapsed state, decision-making is modeled using a simplified DDM, where specific parameters are fixed to approximate random guessing with equal probability. The proposed method is implemented using a computationally efficient generalized expectation-maximization (EM) algorithm with forward-backward procedures. Through extensive numerical studies, we demonstrate that our proposed method outperforms competing approaches across various reward-generating distributions, under both strategy-switching and non-switching scenarios, as well as in the presence of input perturbations. When applied to the EMBARC study, our framework reveals that MDD patients exhibit lower overall engagement than healthy controls and experience longer responses when they do engage. Additionally, we show that neuroimaging measures of brain activities are associated with decision-making characteristics in the engaged state but not in the lapsed state, providing evidence of brain-behavior association specific to the engaged state.

Panel vector autoregressive (VAR) models are effective tools for capturing temporal relationships between a set of variables (e.g., macroeconomic indicators of an economy) while accounting for interdependencies between a set of entities (e.g., market sectors or whole economies). For modeling macroeconomic data, this challenge is often further accentuated by the presence of variables observed at different frequencies. Existing Bayesian approaches that link entity-specific VAR models often impose strict fusion of VAR coefficients to a common value across entities. This paper develops a balanced and less stringent Bayesian approach for mixed frequency panel VAR models that employ group shrinkage prior distributions to borrow strength across entities while allowing for entity-specific idiosyncrasies. A key novel feature is the ability to incorporate and learn the interdependence structure between entities through an interentity covariance (matrix) parameter. The proposed methodology is evaluated both on synthetic data and on two economic applications: employment indices across neighboring U.S. states and macroeconomic indicators of tightly integrated European economies. Finally, we establish the theoretical properties of the proposed approach.

Monitoring the distribution and abundance of plants is important for assessing the environmental impact of anthropogenic activity. Plant cover data are commonly collected using the point intercept method which records species presence or absence at points on a grid overlaying sampled plots. Multiple species are surveyed simultaneously, resulting in binary data that are both multiscale and multivariate. Fine scale and multivariate characteristics are not accounted for with current spatial models for binary data. For instance, conventional analyses of point intercept data discard the subplot scale information by aggregating observations to the plot level. We develop an alternative model for plant cover using clipped multiscale spatial Gaussian processes. Our model allows us to leverage the spatial configuration of observed points within a plot to model small-scale spatial structure in plant cover as well as the large-scale spatial structure among plots. To analyze data from multiple species, we include interspecies correlations that describe community structure. We develop a computationally efficient algorithm for fitting our model using Bayesian methods. We apply our model to analyze lichen and plant cover data collected at Cape Krusenstern National Monument, Alaska, U.S.A., and assess how the vegetation in this region is impacted by heavy metal pollution.

There is keen interest in the field of biomechanics to identify unique strategies of negotiating specific movement tasks post lower-limb joint injury. Finite mixture models are flexible methods that are commonly used for carrying out this type of task. A recent focus in the model-based clustering literature is to highlight the difference between the number of components in a mixture model and the number of clusters. The number of clusters is more relevant from a practical stand point, but to date, the focus of prior distribution formulation has been on the number of components. This can make prior elicitation on the number of clusters challenging when prior information exists, which is the case in the biomechanic study considered here. In light of this, we develop a finite mixture methodology that permits eliciting prior information directly on the number of clusters in a flexible and intuitive way. This is done by employing an asymmetric Dirichlet distribution as a prior on the weights of a finite mixture. Further, a penalized complexity motivated prior is employed for the Dirichlet shape parameter. We illustrate the ease to which prior information can be elicited via our construction and the flexibility of the resulting induced prior on the number of clusters. In addition to applying the method to the biomechanic data, we also demonstrate utility using numerical experiments and the galaxies dataset.