Browsing Department of Biostatistics and Epidemiology: Theses andDissertations by Authors
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A Bayesian Framework To Detect Differentially Methylated Loci in Both Mean And Variability with Next Generation SequencingLi, Shuang; Department of Biostatistics and Epidemiology (201507)DNA methylation at CpG loci is the best known epigenetic process involved in many complex diseases including cancer. In recent years, nextgeneration sequencing (NGS) has been widely used to generate genomewide DNA methylation data. Although substantial evidence indicates that di erence in mean methylation proportion between normal and disease is meaningful, it has recently been proposed that it may be important to consider DNA methylation variability underlying common complex disease and cancer. We introduce a robust hierarchical Bayesian framework with a Latent Gaussian model which incorporates both mean and variance to detect di erentially methylated loci for NGS data. To identify methylation loci which are associated with disease, we consider Bayesian statistical hypotheses testing for methylation mean and methylation variance using a twodimensional highest posterior density region. To improve computational e ciency, we use Integrated Nested Laplace Approximation (INLA), which combines Laplace approximations and numerical integration in a very e cient manner for deriving marginal posterior distributions. We performed simulations to compare our proposed method to other alternative methods. The simulation results illustrate that our proposed approach is more powerful in that it detects less false positives and it has true positive rate comparable to the other methods.

Bayesian Functional Clustering and VMR Identification in Methylation Microarray DataCampbell, Jeff; Department of Biostatistics and Epidemiology (201507)The study of the relation between DNA and health and disease has had a lot of time, energy, and money invested in it over the years. As more scientific knowledge has accumulated, it has become clear that the relations between DNA and health isn’t just a function of the sequence of nucleotide bases, but also on permanent modifications of DNA that affect DNA transcriptions and thus have a macroscopic effect on an individual. The study of modifications to DNA is known as epigenetics.Epigenetic changes have been shown to play a role in certain diseases, including cancer (Novak 2004). Finding locations of differential methylation in two groups of cells is an ongoing area of research in both science and bioinformatics. The number of developed statistical methods for establishing differential DNA methylation between two groups is limited (Bock 2012). Many developed methods are developed for nextgeneration sequencing data and may not work for microarray data, and vice versa. Bisulfite sequencing, the nextgeneration sequencing technique for attaining methylation data, often comes with limited sample size and considerations must be made for low and variable coverage, and smoothing the methylation values. The analysis of nextgeneration sequencing data also involves small sample sizes.In addition, these methods can be sensitive to how individual CpG regions are grouped together as a region for analysis. If the DMRs are small relative to the sizes of 5 established regions, then the method may not detect a region as having differential methylation. Robust methods for clustering microarray data have also been an ongoing area of research. It is desirable to have a method that could be applied to microarray data could increase the sample size and mitigate the previous problems if the method used is robust to missing values, outliers, and microarray data noise. Functional clustering has shown to be effective when properly conducted on gene expression data. It can be used when the data have temporal measurements to identify genes that are possibly coexpressed. The clustering of methylation data can also be shown to identify epigenetic subgroups that can potentially be very useful (Wang, 2011). [introduction]

Classifying Rheumatoid Arthritis Risk with Genetic Subgroups Using GenomeWide AssociationLetter, Abraham J.; Department of Biostatistics and Epidemiology (201004)Structured genomewide association methods can be used to find population substructure, determine significant SNPs, and subsequently narrow down the field of SNPs to those most significant for determining disease risk. Beginning with more than 500,000 SNPs and rheumatoid arthritis (RA) phenotype data for cases and controls, we used a threepart clustering approach that found 684 SNPs significant for determining RA after accounting for clusters, and of those, 168 SNPs with differing odds across clusters. These 168 SNPs were used to create 16 population subgroups, each revealing a unique pattern of minor allele frequencies. The subgroups showed some commonality in multidimensional scaling plots, however, and were combined into five RA risk categories, each with odds differing from the other categories with pvalues less than 0.0001. Thus, based on SNP information from 168 SNPs it may be possible to assign an individual into one of five distinct RA risk categories.

Correlation Coefficient Inference for LeftCensored Biomarker Data with Known Detection LimitsMcCracken, Courtney Elizabeth; Department of Biostatistics and Epidemiology (201305)Researchers are often interested in the relationship between biological concentrations obtained using two different assays, both of which may be biomarkers. Despite the continuing advances in biotechnology, the value of a particular biomarker may fall below some known limit of detection (LOD). Data values such as these are referred to as nondetects (NDs) and can be treated as leftcensored observations. When attempting to measure the association between two concentrations, both of which are subject to NDs, serious complications can arise in the data analysis. Simple substitution, random imputation, and maximum likelihood estimation methods are just a few of the methods that have been proposed for handling NDs when estimating the correlation between two variables, both of which are subject to leftcensoring. Unfortunately, many of the popular methods require that the data follow a bivariate normal distribution or that only a small percentage of the data for each variable are below the LOD. These assumptions are often violated with biomarker data. In this paper, we evaluate the performance of several methods, including Spearman’s rho, when the data do not follow a bivariate normal distribution and when there are moderate to large censoring proportions in one or both of the variables. We evaluate the performance of seven methods for estimating the correlation, ρ, between two leftcensored variables using bias, median absolute deviation, 95% confidence interval width, and coverage probability under assumptions of various sample sizes, correlations, and censoring proportions. We show that using substitution and imputation methods yields biased estimates of ρ and less than nominal coverage probability under most of the simulation parameters we examined. We recommend the maximum likelihood method for general use even when the data significantly depart from bivariate normality.

False coverage rate  adjusted smoothed bootstrap simultaneous confidence intervals for selected parametersSun, Jing; Department of Biostatistics and Epidemiology (Augusta University, 202005)Many modern applications refer to a large number of populations with high dimensional parameters. Since there are so many parameters, researchers often draw inferences regarding the most significant parameters, which are called selected parameters. Benjamini and Yekutieli (2005) proposed the false coveragestatement rate (FCR) method for multiplicity correction when constructing confidence intervals for only selected parameters. FCR for the confidence interval method is parallel to the concept of the false discovery rate for multiple hypothesis testing. In practice, we typically construct FCRadjusted approximate confidence intervals for selected parameters either using the bootstrap method or the normal approximation method. However, these approximated confidence intervals show higher FCR for small and moderate sample sizes. Therefore, we suggest a novel procedure to construct simultaneous confidence intervals for the selected parameters by using a smoothed bootstrap procedure. We consider a smoothed bootstrap procedure using a kernel density estimator. A pertinent problem associated with the smoothed bootstrap approach is how to choose the unknown bandwidth in some optimal sense. We derive an optimal choice for the bandwidth and the resulting smoothed bootstrap confidence intervals asymptotically to give better control of the FCR than its competitors. We further show that the suggested smoothed bootstrap simultaneous confidence intervals are FCRconsistent if the dimension of data grows no faster than N^3/2. Finite sample performances of our method are illustrated based on empirical studies. Through these empirical studies, it is shown that the proposed method can be successfully applied in practice.

Mathematical and Stochastic Modeling of HIV Immunology and EpidemiologyLee, Tae Jin; Department of Biostatistics and Epidemiology (8/3/2017)In HIV virus dynamics, controlling of viral load and maintaining of CD4 value at a higher level are always primary goals for the providers. In recent years, a new molecule was discovered, namely, eCD4Ig, which mimics CD4 if introduced into the human body and has potential to change existing HIV virus dynamics. Thus, to understand dynamics of viral load, eCD4Ig, CD4 cells, we have developed mathematical models by incorporating interactions between this new molecule and other known immunological, virological information. We further investigated model based speculations for management, and obtained the level of eCD4Ig required for elimination of virus. Next, we built epidemiological model for HIV spread and control among discordant couple through dynamics of PrEP (Preexposure prophylaxis). For this, an actuarial assumptions based stochastic model is used to obtain the mean remaining time of couple to stay as discordant. We generalized single hookup/marriage stochastic model to multiple hookup/marriage model.

A modified bump hunting approach with correlationadjusted kernel weight for detecting differentially methylated regions on the 450K arrayDaniel, Jeannie T; Department of Biostatistics and Epidemiology (8/3/2017)DNA methylation plays an important role in the regulation of gene expression, as hypermethylation is associated with gene silencing. The general purpose of this dissertation is the development of a statistical method, called DMR Detector, for detecting differentially methylated regions (DMRs) on the 450K array. DMR Detector makes three key modifications to an existing method called Bumphunter. The first is what statistic to collect from the initial fitting for further analysis. The second is to perform kernel smoothing under the assumption of correlated errors using a newly proposed correlationadjusted kernel weight. The third is how to define regions of interest. In simulation, the method was shown to have high power comparable to Bumphunter, with consistently lower familywise type I error rate, controlled well below the 0.1 FDR. DMR Detector was applied to real data and was able to detect one DMR that was not detected by Bumphunter.

A Modified Information Criterion in the 1d Fused Lasso for DNA Copy Number Variant Detection using Next Generation Sequencing DataLee, Jaeeun; Department of Biostatistics and Epidemiology (8/3/2017)DNA Copy Number Variations (CNVs) are associated with many human diseases. Recently, CNV studies have been carried out using Next Generation Sequencing (NGS) technology that produces millions of short reads. With NGS reads ratio data, we use the 1d fused lasso regression for CNV detection. Given the number of copy number changes, the corresponding genomic locations are estimated by fitting the 1d fused lasso. Estimation of the number of copy number changes depends on a tuning parameter in the 1d fused lasso. In this dissertation, we propose a new modified Bayesian information criterion, called JMIC, to estimate the optimal tuning parameter in the 1d fused lasso. In theoretical studies, we prove that the number of change points estimated by JMIC converges the true number of changes. Also, our simulation studies show that JMIC outperforms the other criteria considered. Finally, we apply our proposed method to the reads ratio data from the breast tumor cell HCC1954 and its matched cell line provided by Chiang et al. (2009).

Multivariate Poisson Abundance Models for Analyzing Antigen Receptor DataGreene, Joshua C.; Department of Biostatistics and Epidemiology (201305)Antigen receptor data is an important source of information for immunologists that is highly statistically challenging to analyze due to the presence of a huge number of Tcell receptors in mammalian immune systems and the severe undersampling bias associated with the commonly used data collection procedures. Many important immunological questions can be stated in terms of richness and diversity of Tcell subsets under various experimental conditions. This dissertation presents a class of parametric models and uses a special case of them to compare the richness and diversity of antigen receptor populations in mammalian Tcells. The parametric models are based on a representation of the observed receptor counts as a multivariate Poisson abundance model (mPAM). A Bayesian model tting procedure is developed which allows tting of the mPAM parameters with the help of the complete likelihood as opposed to its conditional version which was used previously. The new procedure is shown to be often considerably more e cient (as measured by the amount of Fisher information) in the regions of the mPAM parameter space relevant to modeling Tcell data. A richness estimator based on the special case of the mPAM is shown to be superior to several existing richness estimators from the statistical ecology literature under the severe undersampling conditions encountered in antigen receptor data collection. The comparative diversity analyses based on the mPAM special case yield biologically meaningful results when applied to the Tcell receptor repertoires in mice. It is also shown that the amount of time to implement the Bayesian model tting procedure for the mPAM special case scales well as the dimension increases and that the amount of computational resources required to conduct complete statistical analyses for the mPAM special case can be drastically lower for our Bayesian model tting procedure than for code based on the conditional likelihood approach.

Penalized Least Squares and the Algebraic Statistical Model for Biochemical Reaction NetworksLinder, Daniel F. II; Department of Biostatistics and Epidemiology (201307)Systems biology seeks to understand the formation of macro structures such as cellular processes and higher level cellular phenomena by investigating the interactions of systems’ individual components. For cellular biology, this goal is to understand the dynamic behavior of biological materials within the cell, a container consisting of smaller materials such as mRNA, proteins, enzymes and other intermediates necessary for regulating intracellular functions and chemical species levels. Understanding these cellular dynamics is needed to help develop new drug therapies, which can be targeted to specific molecules or specific genes, in order to perturb the system for a desired result. In this work we develop inferential procedures to estimate reaction rate coefficients in cellular systems of ordinary differential equations (ODEs) from noisy data arising from realizations of molecular trajectories. It is assumed that these systems obey the so called chemical mass action law of kinetics, with corresponding deterministic mass action limit as the system size becomes infinite. The estimation and inference is based on the penalized least squares estimates, where the covariance structure of these estimates corresponds to the solution of a system of coupled nonautonomuous ODEs. Another topic discussed here is that of network topology estimation. The algebraic statistical model (ASM) offers a means of performing this topological inference for the special class of conic networks. We prove that the ASM recovers the true network topology as the number of samples grows without bound, a property known in the literature as sparsistency. We propose a method to extend the ASM to a wider class of networks that are decomposable into multiple cones.

Statistical Methods for reaction NetworksOdubote, Oluseyi Samuel; Department of Biostatistics and EpidemiologyStochastic reaction networks are important tools for modeling many biological phenomena, and understanding these networks is important in a wide variety of applied research, such as in disease treatment and in drug development. Statistical inference about the structure and parameters of reaction networks, sometimes referred to in this setting as model calibration, is often challenging due to intractable likelihoods. Here we utilize an idea similar to that of generalized estimating equations (GEE), which in this context are the socalled martingale estimating equations, for estimation of reaction rates of the network. The variance component is estimated using the approximate variance under the linear noise approximation, which is based on partial dierential equation, or FokkerPlanck equations, which provides an approximation to the exact chemical master equation. The method is applied to data from the plague outbreak at Eyam, England from 16651666 and the COVID19 pandemic data. We show empirically that the proposed method gives good estimates of the parameters in a large volume setting and works well in small volume settings.

TWOSAMPLE TESTS FOR HIGH DIMEMSIONAL MEANS WITH PREPIVOTING and DATA TRANSFORMATIONHellebuyck, Rafael Adriel; Department of Biostatistics and Epidemiology (20190108)Within the medical field, the demand to store and analyze small sample, large variable data has become everabundant. Several twosample tests for equality of means, including the revered Hotelling’s T2 test, have already been established when the combined sample size of both populations exceeds the dimension of the variables. However, tests such as Hotelling’s T2 become either unusable or output small power when the number of variables is greater than the combined sample size. We propose a test using both prepivoting and Edgeworth expansion that maintains high power in this higher dimensional scenario, known as the “large p small n ” problem. Our test’s finite sample performance is compared with other recently proposed tests designed to also handle the “large p small n ” situation. We apply our test to a microarray gene expression data set and report competitive rates for both power and TypeI error.