The primary goal of predictive modeling for compositional microbiome data is to better understand and predict disease susceptibility based on the relative abundance of microbial species. Current approaches in this area often assume a high-dimensional sparse setting, where only a small subset of microbiome features is considered relevant to the outcome. However, in real-world data, both large and small effects frequently coexist, and acknowledging the contribution of smaller effects can significantly enhance predictive performance. To address this challenge, we developed Bayesian Compositional Generalized Linear Mixed Models for Analyzing Microbiome Data (BCGLMM). BCGLMM is capable of identifying both moderate taxa effects and the cumulative impact of numerous minor taxa, which are often overlooked in conventional models. With a sparsity-inducing prior, the structured regularized horseshoe prior, BCGLMM effectively collaborates phylogenetically related moderate effects. The random effect term efficiently captures sample-related minor effects by incorporating sample similarities within its variance-covariance matrix. We fitted the proposed models using Markov Chain Monte Carlo (MCMC) algorithms with rstan. The performance of the proposed method was evaluated through extensive simulation studies, demonstrating its superiority with higher prediction accuracy compared to existing methods. We then applied the proposed method on American Gut Data to predict inflammatory bowel disease (IBD). To ensure reproducibility, the code and data used in this paper are available at https://github.com/Li-Zhang28/BCGLMM .

MOTIVATION: Longitudinal metagenomics data, including both 16S rRNA and whole-metagenome shotgun sequencing data, enhanced our abilities to understand the dynamic associations between the human microbiome and various diseases. However, analytic tools have not been fully developed to simultaneously address the main challenges of longitudinal metagenomics data, i.e. high-dimensionality, dependence among samples and zero-inflation of observed counts. RESULTS: We propose a fast zero-inflated negative binomial mixed modeling (FZINBMM) approach to analyze high-dimensional longitudinal metagenomic count data. The FZINBMM approach is based on zero-inflated negative binomial mixed models (ZINBMMs) for modeling longitudinal metagenomic count data and a fast EM-IWLS algorithm for fitting ZINBMMs. FZINBMM takes advantage of a commonly used procedure for fitting linear mixed models, which allows us to include various types of fixed and random effects and within-subject correlation structures and quickly analyze many taxa. We found that FZINBMM remarkably outperformed in computational efficiency and was statistically comparable with two R packages, GLMMadaptive and glmmTMB, that use numerical integration to fit ZINBMMs. Extensive simulations and real data applications showed that FZINBMM outperformed other previous methods, including linear mixed models, negative binomial mixed models and zero-inflated Gaussian mixed models. AVAILABILITY AND IMPLEMENTATION: FZINBMM has been implemented in the R package NBZIMM, available in the public GitHub repository http://github.com//nyiuab//NBZIMM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Microbiome/metagenomic data have specific characteristics, including varying total sequence reads, over-dispersion, and zero-inflation, which require tailored analytic tools. Many microbiome/metagenomic studies follow a longitudinal design to collect samples, which further complicates the analysis methods needed. A flexible and efficient R package is needed for analyzing processed multilevel or longitudinal microbiome/metagenomic data. RESULTS: NBZIMM is a freely available R package that provides functions for setting up and fitting negative binomial mixed models, zero-inflated negative binomial mixed models, and zero-inflated Gaussian mixed models. It also provides functions to summarize the results from fitted models, both numerically and graphically. The main functions are built on top of the commonly used R packages nlme and MASS, allowing us to incorporate the well-developed analytic procedures into the framework for analyzing over-dispersed and zero-inflated count or proportion data with multilevel structures (e.g., longitudinal studies). The statistical methods and their implementations in NBZIMM particularly address the data characteristics and the complex designs in microbiome/metagenomic studies. The package is freely available from the public GitHub repository https://github.com/nyiuab/NBZIMM . CONCLUSION: The NBZIMM package provides useful tools for complex microbiome/metagenomics data analysis.

SUMMARY: BhGLM is a freely available R package that implements Bayesian hierarchical modeling for high-dimensional clinical and genomic data. It consists of functions for setting up various Bayesian hierarchical models, including generalized linear models (GLMs) and Cox survival models, with four types of prior distributions for coefficients, i.e. double-exponential, Student-t, mixture double-exponential and mixture Student-t. These functions adapt fast and stable algorithms to estimate parameters. BhGLM also provides functions for summarizing results numerically and graphically and for evaluating predictive values. The package is particularly useful for analyzing large-scale molecular data, i.e. detecting disease-associated variables and predicting disease outcomes. We here describe the models, algorithms and associated features implemented in BhGLM. AVAILABILITY AND IMPLEMENTATION: The package is freely available from the public GitHub repository, https://github.com/nyiuab/BhGLM.

BACKGROUND: Group structures among genes encoded in functional relationships or biological pathways are valuable and unique features in large-scale molecular data for survival analysis. However, most of previous approaches for molecular data analysis ignore such group structures. It is desirable to develop powerful analytic methods for incorporating valuable pathway information for predicting disease survival outcomes and detecting associated genes. RESULTS: We here propose a Bayesian hierarchical Cox survival model, called the group spike-and-slab lasso Cox (gsslasso Cox), for predicting disease survival outcomes and detecting associated genes by incorporating group structures of biological pathways. Our hierarchical model employs a novel prior on the coefficients of genes, i.e., the group spike-and-slab double-exponential distribution, to integrate group structures and to adaptively shrink the effects of genes. We have developed a fast and stable deterministic algorithm to fit the proposed models. We performed extensive simulation studies to assess the model fitting properties and the prognostic performance of the proposed method, and also applied our method to analyze three cancer data sets. CONCLUSIONS: Both the theoretical and empirical studies show that the proposed method can induce weaker shrinkage on predictors in an active pathway, thereby incorporating the biological similarity of genes within a same pathway into the hierarchical modeling. Compared with several existing methods, the proposed method can more accurately estimate gene effects and can better predict survival outcomes. For the three cancer data sets, the results show that the proposed method generates more powerful models for survival prediction and detecting associated genes. The method has been implemented in a freely available R package BhGLM at https://github.com/nyiuab/BhGLM .

Motivation: Large-scale molecular data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, standard approaches for omics data analysis ignore the group structure among genes encoded in functional relationships or pathway information. Results: We propose new Bayesian hierarchical generalized linear models, called group spike-and-slab lasso GLMs, for predicting disease outcomes and detecting associated genes by incorporating large-scale molecular data and group structures. The proposed model employs a mixture double-exponential prior for coefficients that induces self-adaptive shrinkage amount on different coefficients. The group information is incorporated into the model by setting group-specific parameters. We have developed a fast and stable deterministic algorithm to fit the proposed hierarchal GLMs, which can perform variable selection within groups. We assess the performance of the proposed method on several simulated scenarios, by varying the overlap among groups, group size, number of non-null groups, and the correlation within group. Compared with existing methods, the proposed method provides not only more accurate estimates of the parameters but also better prediction. We further demonstrate the application of the proposed procedure on three cancer datasets by utilizing pathway structures of genes. Our results show that the proposed method generates powerful models for predicting disease outcomes and detecting associated genes. Availability and implementation: The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Molecular analyses suggest that myeloma is composed of distinct sub-types that have different molecular pathologies and various response rates to certain treatments. Drug responses in multiple myeloma (MM) are usually recorded as a multi-level ordinal outcome. One of the goals of drug response studies is to predict which response category any patients belong to with high probability based on their clinical and molecular features. However, as most of genes have small effects, gene-based models may provide limited predictive accuracy. In that case, methods for predicting multi-level ordinal drug responses by incorporating biological pathways are desired but have not been developed yet. Results: We propose a pathway-structured method for predicting multi-level ordinal responses using a two-stage approach. We first develop hierarchical ordinal logistic models and an efficient quasi-Newton algorithm for jointly analyzing numerous correlated variables. Our two-stage approach first obtains the linear predictor (called the pathway score) for each pathway by fitting all predictors within each pathway using the hierarchical ordinal logistic approach, and then combines the pathway scores as new predictors to build a predictive model. We applied the proposed method to two publicly available datasets for predicting multi-level ordinal drug responses in MM using large-scale gene expression data and pathway information. Our results show that our approach not only significantly improved the predictive performance compared with the corresponding gene-based model but also allowed us to identify biologically relevant pathways. Availability and implementation: The proposed approach has been implemented in our R package BhGLM, which is freely available from the public GitHub repository https://github.com/abbyyan3/BhGLM.

MOTIVATION: Large-scale molecular profiling data have offered extraordinary opportunities to improve survival prediction of cancers and other diseases and to detect disease associated genes. However, there are considerable challenges in analyzing large-scale molecular data. RESULTS: We propose new Bayesian hierarchical Cox proportional hazards models, called the spike-and-slab lasso Cox, for predicting survival outcomes and detecting associated genes. We also develop an efficient algorithm to fit the proposed models by incorporating Expectation-Maximization steps into the extremely fast cyclic coordinate descent algorithm. The performance of the proposed method is assessed via extensive simulations and compared with the lasso Cox regression. We demonstrate the proposed procedure on two cancer datasets with censored survival outcomes and thousands of molecular features. Our analyses suggest that the proposed procedure can generate powerful prognostic models for predicting cancer survival and can detect associated genes. AVAILABILITY AND IMPLEMENTATION: The methods have been implemented in a freely available R package BhGLM ( http://www.ssg.uab.edu/bhglm/ ). CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Recent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. In addition to the well-known properties of microbiome count measurements, for example, varied total sequence reads across samples, over-dispersion and zero-inflation, microbiome studies usually collect samples with hierarchical structures, which introduce correlation among the samples and thus further complicate the analysis and interpretation of microbiome count data. RESULTS: In this article, we propose negative binomial mixed models (NBMMs) for detecting the association between the microbiome and host environmental/clinical factors for correlated microbiome count data. Although having not dealt with zero-inflation, the proposed mixed-effects models account for correlation among the samples by incorporating random effects into the commonly used fixed-effects negative binomial model, and can efficiently handle over-dispersion and varying total reads. We have developed a flexible and efficient IWLS (Iterative Weighted Least Squares) algorithm to fit the proposed NBMMs by taking advantage of the standard procedure for fitting the linear mixed models. CONCLUSIONS: We evaluate and demonstrate the proposed method via extensive simulation studies and the application to mouse gut microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of both empirical power and Type I error. The method has been incorporated into the freely available R package BhGLM ( http://www.ssg.uab.edu/bhglm/ and http://github.com/abbyyan3/BhGLM ), providing a useful tool for analyzing microbiome data.

UNLABELLED: R/qtlbim is an extensible, interactive environment for the Bayesian Interval Mapping of QTL, built on top of R/qtl (Broman et al., 2003), providing Bayesian analysis of multiple interacting quantitative trait loci (QTL) models for continuous, binary and ordinal traits in experimental crosses. It includes several efficient Markov chain Monte Carlo (MCMC) algorithms for evaluating the posterior of genetic architectures, i.e. the number and locations of QTL, their main and epistatic effects and gene-environment interactions. R/qtlbim provides extensive informative graphical and numerical summaries, and model selection and convergence diagnostics of the MCMC output, illustrated through the vignette, example and demo capabilities of R (R Development Core Team 2006). AVAILABILITY: The package is freely available from cran.r-project.org.