The Cancer Genome Atlas (TCGA) program has produced huge amounts of cancer genomics data providing unprecedented opportunities for research. In 2014, we developed TCGA-Assembler (Zhu et al., 2014), a software pipeline for retrieval and processing of public TCGA data. In 2016, TCGA data were transferred from the TCGA data portal to the Genomic Data Commons (GDC), which is supported by a different set of data storage and retrieval mechanisms. In addition, new proteomics data of TCGA samples have been generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) program, which were not available for downloading through TCGA-Assembler. It is desirable to acquire and integrate data from both GDC and CPTAC. We develop TCGA-Assembler 2 (TA2) to automatically download and integrate data from GDC and CPTAC. We make substantial improvement on the functionality of TA2 to enhance user experience and software performance. TA2 together with its previous version have helped more than 2,000 researchers from 64 countries to access and utilize TCGA and CPTAC data in their research. Availability of TA2 will continue to allow existing and new users to conduct reproducible research based on TCGA and CPTAC data. http://www.compgenome.org/TCGA-Assembler/ or https://github.com/compgenome365/TCGA-Assembler-2 zhuyitan@gmail.com or koaeraser@gmail.com Supplementary data are available at Bioinformatics online.

We develop a general class of Bayesian repulsive Gaussian mixture models that encourage well-separated clusters, aiming at reducing potentially redundant components produced by independent priors for locations (such as the Dirichlet process). The asymptotic results for the posterior distribution of the proposed models are derived, including posterior consistency and posterior contraction rate in the context of nonparametric density estimation. More importantly, we show that compared to the independent prior on the component centers, the repulsive prior introduces additional shrinkage effect on the tail probability of the posterior number of components, which serves as a measurement of the model complexity. In addition, a generalized urn model that allows a random number of components and correlated component centers is developed based on the exchangeable partition distribution, which gives rise to the corresponding blocked-collapsed Gibbs sampler for posterior inference. We evaluate the performance and demonstrate the advantages of the proposed methodology through extensive simulation studies and real data analysis.

Predicting the clinical trajectory of individual patients hospitalized with COVID-19 is necessary to inform clinical care. This article describes the development of an online tool to predict severe... Visual Development of Severe COVID-19 Adaptive Risk Predictor. Predicting the clinical trajectory of individual patients hospitalized with COVID-19 is necessary to inform clinical care. Th... Background: Predicting the clinical trajectory of individual patients hospitalized with coronavirus disease 2019 (COVID-19) is challenging but necessary to inform clinical care. The majority of COV...

We develop a Bayesian approach called the Bayesian projected calibration to address the problem of calibrating an imperfect computer model using observational data from an unknown complex physical system. The calibration parameter and the physical system are parametrized in an identifiable fashion via the L₂-projection. The physical system is imposed a Gaussian process prior distribution, which naturally induces a prior distribution on the calibration parameter through the L₂-projection constraint. The calibration parameter is estimated through its posterior distribution, serving as a natural and non-asymptotic approach for the uncertainty quantification. We provide rigorous large sample justifications of the proposed approach by establishing the asymptotic normality of the posterior of the calibration parameter with the efficient covariance matrix. In addition to the theoretical analysis, two convenient computational algorithms based on stochastic approximation are designed with strong theoretical support. Through extensive simulation studies and the analyses of two real-world datasets, we show that the proposed Bayesian projected calibration can accurately estimate the calibration parameters, calibrate the computer models well, and compare favorably to alternative approaches.

Background: HIV infection results in immunometabolic reprogramming. While we are beginning to understand how this metabolic reprogramming regulates the immune response to HIV infection, we do not currently understand the impact of ART on immunometabolism in people with HIV (PWH). Methods: Serum obtained from HIV-infected (n = 278) and geographically matched HIV seronegative control subjects (n = 300) from Rakai Uganda were used in this study. Serum was obtained before and ~2 years following the initiation of ART from HIV-infected individuals. We conducted metabolomics profiling of the serum and focused our analysis on metabolic substrates and pathways assocaited with immunometabolism. Results: HIV infection was associated with metabolic adaptations that implicated hyperactive glycolysis, enhanced formation of lactate, increased activity of the pentose phosphate pathway (PPP), decreased β-oxidation of long-chain fatty acids, increased utilization of medium-chain fatty acids, and enhanced amino acid catabolism. Following ART, serum levels of ketone bodies, carnitine, and amino acid metabolism were normalized, however glycolysis, PPP, lactate production, and β-oxidation of long-chain fatty acids remained abnormal. Conclusion: Our findings suggest that HIV infection is associated with an increased immunometabolic demand that is satisfied through the utilization of alternative energetic substrates, including fatty acids and amino acids. ART alone was insufficient to completely restore this metabolic reprogramming to HIV infection, suggesting that a sustained impairment of immunometabolism may contribute to chronic immune activation and comorbid conditions in virally suppressed PWH.