A dynamic Bayesian nonlinear mixed-effects model of HIV response incorporating medication adherence, drug resistance and covariates

HIV dynamic studies have contributed significantly to the understanding of HIV pathogenesis and antiviral treatment strategies for AIDS patients. Establishing the relationship of virologic responses with clinical factors and covariates during long-term antiretroviral (ARV) therapy is important to the development of effective treatments. Medication adherence is an important predictor of the effectiveness of ARV treatment, but an appropriate determinant of adherence rate based on medication event monitoring system (MEMS) data is critical to predict virologic outcomes. The primary objective of this paper is to investigate the effects of a number of summary determinants of MEMS adherence rates on virologic response measured repeatedly over time in HIV-infected patients. We developed a mechanism-based differential equation model with consideration of drug adherence, interacted by virus susceptibility to drug and baseline characteristics, to characterize the long-term virologic responses after initiation of therapy. This model fully integrates viral load, MEMS adherence, drug resistance and baseline covariates into the data analysis. In this study we employed the proposed model and associated Bayesian nonlinear mixed-effects modeling approach to assess how to efficiently use the MEMS adherence data for prediction of virologic response, and to evaluate the predicting power of each summary metric of the MEMS adherence rates.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS376 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Multi-way blockmodels for analyzing coordinated high-dimensional responses

We consider the problem of quantifying temporal coordination between multiple high-dimensional responses. We introduce a family of multi-way stochastic blockmodels suited for this problem, which avoids preprocessing steps such as binning and thresholding com- monly adopted for this type of data, in biology. We develop two in- ference procedures based on collapsed Gibbs sampling and variational methods. We provide a thorough evaluation of the proposed methods on simulated data, in terms of membership and blockmodel estima- tion, predictions out-of-sample and run-time. We also quantify the effects of censoring procedures such as binning and thresholding on the estimation tasks. We use these models to carry out an empirical analysis of the functional mechanisms driving the coordination be- tween gene expression and metabolite concentrations during carbon and nitrogen starvation, in S. cerevisiae.Statistic

PENALIZED LIKELIHOOD AND BAYESIAN METHODS FOR SPARSE CONTINGENCY TABLES: AN ANALYSIS OF ALTERNATIVE SPLICING IN FULL-LENGTH cDNA LIBRARIES

We develop methods to perform model selection and parameter estimation in loglinear models for the analysis of sparse contingency tables to study the interaction of two or more factors. Typically, datasets arising from so-called full-length cDNA libraries, in the context of alternatively spliced genes, lead to such sparse contingency tables. Maximum Likelihood estimation of log-linear model coefficients fails to work because of zero cell entries. Therefore new methods are required to estimate the coefficients and to perform model selection. Our suggestions include computationally efficient penalization (Lasso-type) approaches as well as Bayesian methods using MCMC. We compare these procedures in a simulation study and we apply the proposed methods to full-length cDNA libraries, yielding valuable insight into the biological process of alternative splicing

Bayesian nonparametric clusterings in relational and high-dimensional settings with applications in bioinformatics.

Recent advances in high throughput methodologies offer researchers the ability to understand complex systems via high dimensional and multi-relational data. One example is the realm of molecular biology where disparate data (such as gene sequence, gene expression, and interaction information) are available for various snapshots of biological systems. This type of high dimensional and multirelational data allows for unprecedented detailed analysis, but also presents challenges in accounting for all the variability. High dimensional data often has a multitude of underlying relationships, each represented by a separate clustering structure, where the number of structures is typically unknown a priori. To address the challenges faced by traditional clustering methods on high dimensional and multirelational data, we developed three feature selection and cross-clustering methods: 1) infinite relational model with feature selection (FIRM) which incorporates the rich information of multirelational data; 2) Bayesian Hierarchical Cross-Clustering (BHCC), a deterministic approximation to Cross Dirichlet Process mixture (CDPM) and to cross-clustering; and 3) randomized approximation (RBHCC), based on a truncated hierarchy. An extension of BHCC, Bayesian Congruence Measuring (BCM), is proposed to measure incongruence between genes and to identify sets of congruent loci with identical evolutionary histories. We adapt our BHCC algorithm to the inference of BCM, where the intended structure of each view (congruent loci) represents consistent evolutionary processes. We consider an application of FIRM on categorizing mRNA and microRNA. The model uses latent structures to encode the expression pattern and the gene ontology annotations. We also apply FIRM to recover the categories of ligands and proteins, and to predict unknown drug-target interactions, where latent categorization structure encodes drug-target interaction, chemical compound similarity, and amino acid sequence similarity. BHCC and RBHCC are shown to have improved predictive performance (both in terms of cluster membership and missing value prediction) compared to traditional clustering methods. Our results suggest that these novel approaches to integrating multi-relational information have a promising future in the biological sciences where incorporating data related to varying features is often regarded as a daunting task

Altered developmental programming of the mouse mammary gland in female offspring following perinatal dietary exposures : a systems-biology perspective.

Mishaps in prenatal development can influence mammary gland development and, ultimately, affect susceptibility to factors that cause breast cancer. This research was based on the underlying hypothesis that maternal dietary composition during pregnancy can alter developmental (fetal) programming of the mammary gland. We used a computational systems-biology approach and Bayesian-based stochastic search variable selection algorithm (SSVS) to identify differentially expressed genes and biological themes and pathways. Postnatal growth trajectories and gene expression in the mammary gland at 10-weeks of age in female mice were investigated following different maternal diet exposures during prenatal-lactational-early-juvenile development. This correlated a decrease in expression of energy pathways with a reciprocal increase in cytokine and inflammatory-signaling pathways. These findings suggest maternal dietary fat exposure significantly influences postnatal growth trajectories, metabolic programming, and signaling networks in the mammary gland of female offspring. In addition, the adipocytokine pathway may be a sensitive trigger to dietary changes and may influence or enhance activation of an immune response, a key event in cancer development

Modeling gene regulatory networks through data integration

Modeling gene regulatory networks has become a problem of great interest in biology and medical research. Most common methods for learning regulatory dependencies rely on observations in the form of gene expression data. In this dissertation, computational models for gene regulation have been developed based on constrained regression by integrating comprehensive gene expression data for M. tuberculosis with genome-scale ChIP-Seq interaction data. The resulting models confirmed predictive power for expression in independent stress conditions and identified mechanisms driving hypoxic adaptation and lipid metabolism in M. tuberculosis. I then used the regulatory network model for M. tuberculosis to identify factors responding to stress conditions and drug treatments, revealing drug synergies and conditions that potentiate drug treatments. These results can guide and optimize design of drug treatments for this pathogen. I took the next step in this direction, by proposing a new probabilistic framework for learning modular structures in gene regulatory networks from gene expression and protein-DNA interaction data, combining the ideas of module networks and stochastic blockmodels. These models also capture combinatorial interactions between regulators. Comparisons with other network modeling methods that rely solely on expression data, showed the essentiality of integrating ChIP-Seq data in identifying direct regulatory links in M. tuberculosis. Moreover, this work demonstrates the theoretical advantages of integrating ChIP-Seq data for the class of widely-used module network models. The systems approach and statistical modeling presented in this dissertation can also be applied to problems in other organisms. A similar approach was taken to model the regulatory network controlling genes with circadian gene expression in Neurospora crassa, through integrating time-course expression data with ChIP-Seq data. The models explained combinatorial regulations leading to different phase differences in circadian rhythms. The Neurospora crassa network model also works as a tool to manipulate the phases of target genes

Inferring Covariance Structure from Multiple Data Sources via Subspace Factor Analysis

Factor analysis provides a canonical framework for imposing lower-dimensional structure such as sparse covariance in high-dimensional data. High-dimensional data on the same set of variables are often collected under different conditions, for instance in reproducing studies across research groups. In such cases, it is natural to seek to learn the shared versus condition-specific structure. Existing hierarchical extensions of factor analysis have been proposed, but face practical issues including identifiability problems. To address these shortcomings, we propose a class of SUbspace Factor Analysis (SUFA) models, which characterize variation across groups at the level of a lower-dimensional subspace. We prove that the proposed class of SUFA models lead to identifiability of the shared versus group-specific components of the covariance, and study their posterior contraction properties. Taking a Bayesian approach, these contributions are developed alongside efficient posterior computation algorithms. Our sampler fully integrates out latent variables, is easily parallelizable and has complexity that does not depend on sample size. We illustrate the methods through application to integration of multiple gene expression datasets relevant to immunology

Bayesian analytical approaches for metabolomics : a novel method for molecular structure-informed metabolite interaction modeling, a novel diagnostic model for differentiating myocardial infarction type, and approaches for compound identification given mass spectrometry data.

Metabolomics, the study of small molecules in biological systems, has enjoyed great success in enabling researchers to examine disease-associated metabolic dysregulation and has been utilized for the discovery biomarkers of disease and phenotypic states. In spite of recent technological advances in the analytical platforms utilized in metabolomics and the proliferation of tools for the analysis of metabolomics data, significant challenges in metabolomics data analyses remain. In this dissertation, we present three of these challenges and Bayesian methodological solutions for each. In the first part we develop a new methodology to serve a basis for making higher order inferences in metabolomics, which we define as the testing of hypotheses that are more complex than single metabolite hypothesis tests. This methodology utilizes informative priors that are generated via the analysis of molecular structure similarity to enable the estimation of metabolite interactomes (or probabilistic models) which are organism-, sample media-, and condition-specific as well as comprehensive; and that can serve as reference models for studying perturbations in metabolic systems. After discussing the development of our methodology, we present an evaluation of its performance conducted using simulation studies, and we use the methodology for estimating a plasma metabolite interactome for stable heart disease. This interactome may serve as a reference model for evaluating systems-level changes that occur with acute disease events such as myocardial infarction (MI) or unstable angina. In the second part of this work, we present the challenge of developing diagnostic classification models which utilize metabolite abundances and that do not overfit relatively small sample sizes, especially given the high dimensionality of metabolite data acquired using platforms such as liquid chromatography-mass spectrometry. We use a Bayesian methodology for estimating a multinomial logistic regression classifier for the detection and discrimination of the subtype of acute myocardial infarction utilizing metabolite abundance data quantified from blood plasma. As heart disease is the leading cause of global mortality, a blood-based and non-invasive diagnostic test that could differentiate between MI types at the time of the event would have great utility. In the final part of this dissertation we review Bayesian approaches for compound identification in metabolomics experiments that utilize liquid chromatography-mass spectrometry which remains a challenging problem