Adaptive Bayesian variable clustering via structural learning of breast cancer data.

The clustering of proteins is of interest in cancer cell biology. This article proposes a hierarchical Bayesian model for protein (variable) clustering hinging on correlation structure. Starting from a multivariate normal likelihood, we enforce the clustering through prior modeling using angle-based unconstrained reparameterization of correlations and assume a truncated Poisson distribution (to penalize a large number of clusters) as prior on the number of clusters. The posterior distributions of the parameters are not in explicit form and we use a reversible jump Markov chain Monte Carlo based technique is used to simulate the parameters from the posteriors. The end products of the proposed method are estimated cluster configuration of the proteins (variables) along with the number of clusters. The Bayesian method is flexible enough to cluster the proteins as well as estimate the number of clusters. The performance of the proposed method has been substantiated with extensive simulation studies and one protein expression data with a hereditary disposition in breast cancer where the proteins are coming from different pathways.

A Bayesian survival treed hazards model using latent Gaussian processes.

Survival models are used to analyze time-to-event data in a variety of disciplines. Proportional hazard models provide interpretable parameter estimates, but proportional hazard assumptions are not always appropriate. Non-parametric models are more flexible but often lack a clear inferential framework. We propose a Bayesian treed hazards partition model that is both flexible and inferential. Inference is obtained through the posterior tree structure and flexibility is preserved by modeling the log-hazard function in each partition using a latent Gaussian process. An efficient reversible jump Markov chain Monte Carlo algorithm is accomplished by marginalizing the parameters in each partition element via a Laplace approximation. Consistency properties for the estimator are established. The method can be used to help determine subgroups as well as prognostic and/or predictive biomarkers in time-to-event data. The method is compared with some existing methods on simulated data and a liver cirrhosis dataset.

Bayesian structural equation modeling in multiple omics data with application to circadian genes.

MOTIVATION: It is well known that the integration among different data-sources is reliable because of its potential of unveiling new functionalities of the genomic expressions, which might be dormant in a single-source analysis. Moreover, different studies have justified the more powerful analyses of multi-platform data. Toward this, in this study, we consider the circadian genes' omics profile, such as copy number changes and RNA-sequence data along with their survival response. We develop a Bayesian structural equation modeling coupled with linear regressions and log normal accelerated failure-time regression to integrate the information between these two platforms to predict the survival of the subjects. We place conjugate priors on the regression parameters and derive the Gibbs sampler using the conditional distributions of them. RESULTS: Our extensive simulation study shows that the integrative model provides a better fit to the data than its closest competitor. The analyses of glioblastoma cancer data and the breast cancer data from TCGA, the largest genomics and transcriptomics database, support our findings. AVAILABILITY AND IMPLEMENTATION: The developed method is wrapped in R package available at https://github.com/MAITYA02/semmcmc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Circadian Gene Selection for Time-to-event Phenotype by Integrating CNV and RNAseq Data.

BACKGROUND: The endogenous circadian clock, which controls daily rhythms in the expression of at least half of the mammalian genome, has a major influence on cell physiology. Consequently, disruption of the circadian system is associated with wide range of diseases including cancer. While several circadian clock genes have been associated with cancer progression, little is known about the survival when two or more platforms are considered together. Our goal was to determine if survival outcomes are associated with circadian clock function. To accomplish this goal, we developed a Bayesian hierarchical survival model coupled with the global local shrinkage prior and applied this model to available RNASeq and Copy Number Variation data to select significant circadian genes associates with cancer progression. RESULTS: Using a Bayesian shrinkage approach with the Bayesian accelerated failure time (AFT) model we showed the circadian clock associated gene DEC1 is positively correlated to survival outcome in breast cancer patients. The R package circgene implementing the methodology is available at https://github.com/MAITYA02/circgene. CONCLUSIONS: The proposed Bayesian hierarchical model is the first shrinkage prior based model in its kind which integrates two omics platforms to identify the significant circadian gene for cancer survival.

Dietary Intakes of Amino Acids and Other Nutrients by Adult Humans.

Measuring usual dietary intake in freely living humans is difficult to accomplish. As a part of our recent study, a food frequency questionnaire was completed by healthy adult men and women at days 0 and 90 of the study. Data from the food questionnaire were analyzed with a nutrient analysis program ( www.Harvardsffq.date ). Healthy men and women consumed protein as 19-20% and 17-19% of their total energy intakes, respectively, with animal protein representing about 75 and 70% of their total protein intakes, respectively. The intake of each nutritionally essential amino acid (EAA) by the persons exceeded that recommended for healthy adults with a minimal physical activity. In all individuals, the dietary intake of leucine was the highest, followed by lysine, valine, and isoleucine in descending order, and the ingestion of amino acids that are synthesizable de novo in animal cells (AASAs) was about 20% greater than that of total EAAs. The intake of each AASA met those recommended for healthy adults with a minimal physical activity. Intakes of some AASAs (alanine, arginine, aspartate, glutamate, and glycine) from a typical diet providing 90-110 g food protein/day does not meet the requirements of adults with an intensive physical activity. Within the male or female group, there were not significant differences in the dietary intakes of all amino acids between days 0 and 90 of the study, and this was also true for nearly all other essential nutrients. Our findings will help to improve amino acid nutrition and health in both the general population and exercising individuals.

Bayesian graph selection consistency under model misspecification.

Gaussian graphical models are a popular tool to learn the dependence structure in the form of a graph among variables of interest. Bayesian methods have gained in popularity in the last two decades due to their ability to simultaneously learn the covariance and the graph. There is a wide variety of model-based methods to learn the underlying graph assuming various forms of the graphical structure. Although for scalability of the Markov chain Monte Carlo algorithms, decomposability is commonly imposed on the graph space, its possible implication on the posterior distribution of the graph is not clear. An open problem in Bayesian decomposable structure learning is whether the posterior distribution is able to select a meaningful decomposable graph that is "close" to the true non-decomposable graph, when the dimension of the variables increases with the sample size. In this article, we explore specific conditions on the true precision matrix and the graph, which results in an affirmative answer to this question with a commonly used hyper-inverse Wishart prior on the covariance matrix and a suitable complexity prior on the graph space. In absence of structural sparsity assumptions, our strong selection consistency holds in a high-dimensional setting where p = O(nα ) for α < 1/3. We show when the true graph is non-decomposable, the posterior distribution concentrates on a set of graphs that are minimal triangulations of the true graph.

Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data.

Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated "The Cancer Proteome Atlas" (TCPA), which contains reverse-phase protein arrays-based high-quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.

Japanese encephalitis associated acute encephalitis syndrome cases in West Bengal, India: A sero-molecular evaluation in relation to clinico-pathological spectrum.

Japanese encephalitis (JE) is a major public health problem in Asia and worldwide and it is responsible mainly for viral acute encephalitis syndrome (AES). The sole etiologic agent of JE is Japanese encephalitis virus (JEV). Although JE/AES cases have been regarded traditionally as a disease of children, a growing number of patients with JE/AES cases are also seen in the adult age group every year in the state of West Bengal, India in spite of vaccination. Therefore, a systematic study was performed to differentiate and characterize the clinico-pathological parameters and viral diversity among the patients of different age groups. Viral diversity was also evaluated from the JE/AES cases, depending on their disease severity. A total of 441 JE/AES cases were included in this study. By MAC-ELISA, 111 samples were found JEV IgM positive and among the IgM negative cases, 26 samples were found RT-PCR positive against JEV infection. Neck rigidity, abnormal behavior, convulsion, protein in CSF, WBC in CSF, and aspartate transaminase in blood differed significantly among the patients of pediatric-adolescent and adult group in both IgM positive and RT-PCR positive cases. Viral diversity was increased significantly in the pediatric-adolescent group compared to adult patients. Interestingly, with the rise in disease severity the viral diversity was found to be increased among the patients, irrespective of their age distribution. Based on clinico-pathological parameters and analysis of viral diversity, it can be concluded that viral diversity which occurs naturally is likely to affect disease severity, especially in the patients of pediatric-adolescent group.

Inferring gene networks from discrete expression data.

The modeling of gene networks from transcriptional expression data is an important tool in biomedical research to reveal signaling pathways and to identify treatment targets. Current gene network modeling is primarily based on the use of Gaussian graphical models applied to continuous data, which give a closed-form marginal likelihood. In this paper, we extend network modeling to discrete data, specifically data from serial analysis of gene expression, and RNA-sequencing experiments, both of which generate counts of mRNA transcripts in cell samples. We propose a generalized linear model to fit the discrete gene expression data and assume that the log ratios of the mean expression levels follow a Gaussian distribution. We restrict the gene network structures to decomposable graphs and derive the graphs by selecting the covariance matrix of the Gaussian distribution with the hyper-inverse Wishart priors. Furthermore, we incorporate prior network models based on gene ontology information, which avails existing biological information on the genes of interest. We conduct simulation studies to examine the performance of our discrete graphical model and apply the method to two real datasets for gene network inference.

Bayesian semiparametric regression in the presence of conditionally heteroscedastic measurement and regression errors.

We consider the problem of robust estimation of the regression relationship between a response and a covariate based on sample in which precise measurements on the covariate are not available but error-prone surrogates for the unobserved covariate are available for each sampled unit. Existing methods often make restrictive and unrealistic assumptions about the density of the covariate and the densities of the regression and the measurement errors, for example, normality and, for the latter two, also homoscedasticity and thus independence from the covariate. In this article we describe Bayesian semiparametric methodology based on mixtures of B-splines and mixtures induced by Dirichlet processes that relaxes these restrictive assumptions. In particular, our models for the aforementioned densities adapt to asymmetry, heavy tails and multimodality. The models for the densities of regression and measurement errors also accommodate conditional heteroscedasticity. In simulation experiments, our method vastly outperforms existing methods. We apply our method to data from nutritional epidemiology.

Joint Bayesian estimation of cell dependence and gene associations in spatially resolved transcriptomic data.

Recent technologies such as spatial transcriptomics, enable the measurement of gene expressions at the single-cell level along with the spatial locations of these cells in the tissue. Spatial clustering of the cells provides valuable insights into the understanding of the functional organization of the tissue. However, most such clustering methods involve some dimension reduction that leads to a loss of the inherent dependency structure among genes at any spatial location in the tissue. This destroys valuable insights of gene co-expression patterns apart from possibly impacting spatial clustering performance. In spatial transcriptomics, the matrix-variate gene expression data, along with spatial coordinates of the single cells, provides information on both gene expression dependencies and cell spatial dependencies through its row and column covariances. In this work, we propose a joint Bayesian approach to simultaneously estimate these gene and spatial cell correlations. These estimates provide data summaries for downstream analyses. We illustrate our method with simulations and analysis of several real spatial transcriptomic datasets. Our work elucidates gene co-expression networks as well as clear spatial clustering patterns of the cells. Furthermore, our analysis reveals that downstream spatial-differential analysis may aid in the discovery of unknown cell types from known marker genes.

Retraction Note: Correlation between COVID-19 and air pollution: the effects of PM2.5 and PM10 on COVID-19 outcomes.

The article "Correlation between COVID-19 and air pollution: the effects of PM2.5 and PM10 on COVID-19 outcomes", by E. Kalluçi, E. Noka, K. Bani, X. Dhamo, I. Alimehmeti, K. Dhuli, G. Madeo, C. Micheletti, G. Bonetti, C. Zuccato, E. Borghetti, G. Marceddu, M. Bertelli, published in Eur Rev Med Pharmacol Sci 2023; 27 (6 Suppl): 39-47-DOI: 10.26355/eurrev_202312_34688-PMID: 38112947 has been retracted by the Editor in Chief. Following concerns raised on PubPeer, the Editor in Chief has initiated an investigation to evaluate the validity of the results. Despite the authors' prompt responses to the identified issues, the Editor in Chief has decided to withdraw the article due to significant errors in the text and final statements, as well as undisclosed conflicts of interest. The Publisher apologizes if these concerns have not been detected during the review process. The authors have been informed about the retraction. This article has been retracted. The Publisher apologizes for any inconvenience this may cause. https://www.europeanreview.org/article/34688.

Joint high-dimensional Bayesian variable and covariance selection with an application to eQTL analysis.

We describe a Bayesian technique to (a) perform a sparse joint selection of significant predictor variables and significant inverse covariance matrix elements of the response variables in a high-dimensional linear Gaussian sparse seemingly unrelated regression (SSUR) setting and (b) perform an association analysis between the high-dimensional sets of predictors and responses in such a setting. To search the high-dimensional model space, where both the number of predictors and the number of possibly correlated responses can be larger than the sample size, we demonstrate that a marginalization-based collapsed Gibbs sampler, in combination with spike and slab type of priors, offers a computationally feasible and efficient solution. As an example, we apply our method to an expression quantitative trait loci (eQTL) analysis on publicly available single nucleotide polymorphism (SNP) and gene expression data for humans where the primary interest lies in finding the significant associations between the sets of SNPs and possibly correlated genetic transcripts. Our method also allows for inference on the sparse interaction network of the transcripts (response variables) after accounting for the effect of the SNPs (predictor variables). We exploit properties of Gaussian graphical models to make statements concerning conditional independence of the responses. Our method compares favorably to existing Bayesian approaches developed for this purpose.

Envelope protein gene based molecular characterization of Japanese encephalitis virus clinical isolates from West Bengal, India: a comparative approach with respect to SA14-14-2 live attenuated vaccine strain.

BACKGROUND: Increasing virulence of Japanese encephalitis virus (JEV), a mosquito-borne zoonotic pathogen is of grave concern because it causes a neurotrophic killer disease Japanese Encephalitis (JE) which, in turn, is responsible globally for viral acute encephalitis syndrome (AES). Despite the availability of vaccine, JE/AES cases and deaths have become regular features in the different rural districts of West Bengal (WB) state, India, indicating either the partial coverage of vaccine or the emergence of new strain of JEV. Therefore, a study was undertaken to characterize and compare the complete envelope (E) protein gene based molecular changes/patterns of JEVs circulating in WB. METHODS: Total of 98 AES case-patients' samples were tested to detect the presence of JEV specific immunoglobulin M (IgM) antibody by Mac-ELISA method. Only JEV IgM negative samples with a history of ≤3 days' illness were screened for virus isolation and RT-PCR. E gene sequences of JEV isolates were subjected to molecular phylogeny and immunoinformatics analysis. RESULTS: Present study confirmed JEV etiology in 39.7% and 29.1% of patients presenting ≤15 days' febrile illness, as determined by Mac-ELISA and RT-PCR respectively. Phylogenetic analysis based on complete E gene sequences of JEV isolates showed the co-circulation of JEV genotype I (GI) with genotype III (GIII). This study also demonstrated that isolate-specific crucial amino acid substitutions were closely related to neurovirulence/neuroinvasiveness of JE. On the basis of immunoinformatics analysis, some substitutions were predicted to disrupt T-cell epitope immunogenicity/antigenicity that might largely influence the outcome of vaccine derived from JEV GIII SA14-14-2 strain and this has been observed in a previously vaccinated boy with mild JE/AES due to JEV GI infection. CONCLUSIONS: Based on molecular evolutionary and bioinformatic approaches, we report evolution of JEV at a local level. Such naturally occurring evolution is likely to affect the disease profile and the vaccine efficacy to protect against JEV GI may demand careful evaluation.

The application of next generation matrix in the calculation of basic reproduction number for COVID-19.

Background: Infectious diseases are disorders caused by microorganisms such as bacteria, viruses, fungi, or parasites. Many organisms live in and on our bodies. They are normally harmless or even helpful. However under certain conditions, some organisms may cause disease. Infectious diseases are also called contagious diseases due to the fact that they can be passed from person to person. Some are transmitted by insects or other animals. COVID-19 is an infectious disease that has "pervaded" the whole world during the last three years. The World Health Organization (WHO) has declared COVID-19 a Public Health Emergency of International Concern. Methods: In this paper, we will study the outbreak of this pandemic in Albania based on some mathematical models, such as SIR, SIRD, and SEIRD. We will present a detailed analysis of these models and also demonstrate how they can be used to predict the spread of infectious diseases. More precisely, we will see the spread of COVID-19 in our country, Albania. Software such as MATLAB and RStudio will be used to do this. The data that we will use when working with these programs is taken from the Institute of Public Health, Tirana, Albania. Results: We've developed an application utilizing actual data to estimate SEIRD model parameters. It's able to compute the basic reproduction number and, more significantly, provides forecasts on the disease's progression. Conclusions: Our aim is to calculate the Basic Reproduction Number, using the Next Generation Matrix, and use it to see the future of the disease. This is the average number of new infections generated by an infected individual. A large value indicates that the infection is transmitted very quickly. We will try to calculate what the values of Basic Number Reproduction have been over different time periods.

Correlation between COVID-19 and air pollution: the effects of PM2.5 and PM10 on COVID-19 outcomes.

OBJECTIVE: Given its effects on long-term illnesses, like heart problems and diabetes, air pollution may be among the reasons that led COVID-19 to get worse and kill a larger number of people. Experiments have shown that breathing in polluted air weakens the immune system, making it easier for viruses to enter the body and grow. Viruses may be able to survive in the air by interacting in complex ways with particles and gases. These interactions depend on the air's chemical makeup, the particles' electric charges, and environmental conditions like humidity, UV light, and temperature. Moreover, exposure to UV rays and air pollution may reduce the organism's production of antimicrobial molecules, thus supporting viral infections. More epidemiological studies are needed to determine what effects air pollution has on COVID-19. In this review, we will discuss how air pollutants such as PM2.5 and PM10 contribute to the transmission of COVID-19. MATERIALS AND METHODS: We have used nine target cities in the Tuscany region to verify this certainty, and in all these cases, the air pollution factors were found to be strongly correlated with COVID-19 cases. For each city, we applied a multivariate analysis and found an appropriate model that better fits the data. RESULTS: This review underlines that both short-term and long-term exposure to air pollution may be crucial exasperating factors for SARS-CoV-2 transmission and COVID-19 severity and lethality. The statistical analysis concludes that air pollution should be accounted for as a possible risk factor in future COVID-19 investigations, and it should be avoided as much as possible by the general population. CONCLUSIONS: Our research highlighted the correlation between COVID-19 and air pollution. Reducing air pollution exposure should be one of the first measures against COVID-19 spread.

Bayesian nonparametric regression analysis of data with random effects covariates from longitudinal measurements.

We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves.

Bayesian Copula Density Deconvolution for Zero-Inflated Data in Nutritional Epidemiology.

Estimating the marginal and joint densities of the long-term average intakes of different dietary components is an important problem in nutritional epidemiology. Since these variables cannot be directly measured, data are usually collected in the form of 24-hour recalls of the intakes, which show marked patterns of conditional heteroscedasticity. Significantly compounding the challenges, the recalls for episodically consumed dietary components also include exact zeros. The problem of estimating the density of the latent long-time intakes from their observed measurement error contaminated proxies is then a problem of deconvolution of densities with zero-inflated data. We propose a Bayesian semiparametric solution to the problem, building on a novel hierarchical latent variable framework that translates the problem to one involving continuous surrogates only. Crucial to accommodating important aspects of the problem, we then design a copula based approach to model the involved joint distributions, adopting different modeling strategies for the marginals of the different dietary components. We design efficient Markov chain Monte Carlo algorithms for posterior inference and illustrate the efficacy of the proposed method through simulation experiments. Applied to our motivating nutritional epidemiology problems, compared to other approaches, our method provides more realistic estimates of the consumption patterns of episodically consumed dietary components.

Hibernating ribosomes exhibit chaperoning activity but can resist unfolded protein-mediated subunit dissociation.

Ribosome hibernation is a prominent cellular strategy to modulate protein synthesis during starvation and the stationary phase of bacterial cell growth. Translational suppression involves the formation of either factor-bound inactive 70S monomers or dimeric 100S hibernating ribosomal complexes, the biological significance of which is poorly understood. Here, we demonstrate that the Escherichia coli 70S ribosome associated with stationary phase factors hibernation promoting factor or protein Y or ribosome-associated inhibitor A and the 100S ribosome isolated from both Gram-negative and Gram-positive bacteria are resistant to unfolded protein-mediated subunit dissociation and subsequent degradation by cellular ribonucleases. Considering that the increase in cellular stress is accompanied by accumulation of unfolded proteins, such resistance of hibernating ribosomes towards dissociation might contribute to their maintenance during the stationary phase. Analysis of existing structures provided clues on the mechanism of inhibition of the unfolded protein-mediated disassembly in case of hibernating factor-bound ribosome. Further, the factor-bound 70S and 100S ribosomes can suppress protein aggregation and assist in protein folding. The chaperoning activity of these ribosomes is the first evidence of a potential biological activity of the hibernating ribosome that might be crucial for cell survival under stress conditions.

Semiparametric bayesian analysis of nutritional epidemiology data in the presence of measurement error.

We propose a semiparametric Bayesian method for handling measurement error in nutritional epidemiological data. Our goal is to estimate nonparametrically the form of association between a disease and exposure variable while the true values of the exposure are never observed. Motivated by nutritional epidemiological data, we consider the setting where a surrogate covariate is recorded in the primary data, and a calibration data set contains information on the surrogate variable and repeated measurements of an unbiased instrumental variable of the true exposure. We develop a flexible Bayesian method where not only is the relationship between the disease and exposure variable treated semiparametrically, but also the relationship between the surrogate and the true exposure is modeled semiparametrically. The two nonparametric functions are modeled simultaneously via B-splines. In addition, we model the distribution of the exposure variable as a Dirichlet process mixture of normal distributions, thus making its modeling essentially nonparametric and placing this work into the context of functional measurement error modeling. We apply our method to the NIH-AARP Diet and Health Study and examine its performance in a simulation study.