PhD, University of Cincinnati, 2007, Medicine : Biostatistics (Environmental Health)

DNA microarrays are a revolutionary technology able to measure the expression levels of thousands of genes simultaneously, providing a snapshot in time of a tissue or cell culture's transcriptome. Although microarrays have been in existence for several years now, research is yet ongoing for how to best analyze the data, at least partly due to the combination of small sample sizes (few replicates) with large numbers of genes. Several challenges remain in maximizing the amount of biological information attainable from a microarray experiment. The key components of microarray analysis where these challenges lie are experimental design, preprocessing, statistical inference, identifying expression patterns, and understanding biological relevance. In this dissertation we aim to improve the analysis and interpretation of microarray data by concentrating on two key steps in microarray analysis: obtaining accurate estimates of significance when testing for differentially expressed genes, and identifying key biological functions and cellular pathways affected by the experimental conditions. We identify opportunities to enhance analytical techniques, and demonstrate that these enhancements significantly improve the functional interpretation of microarray results. We develop three related Bayesian statistical models to improve the estimates of significance by exploiting the information available from all genes, and functionally relating the gene variances to their expression levels. These novel methodologies are compared to previously proposed methods both in simulations and with real-world experimental data performed on multiple microarray platforms. In addition, we introduce a logistic regression method for identifying key biological categories and molecular pathways and compared this method with the commonly used Fisher's exact test and other relevant previously developed methods. We make our statistical methods available to the biomedical research community through the use (open full item for complete abstract)

Committee: Dr. Mario Medvedovic (Advisor) Subjects:

PhD, University of Cincinnati, 2013, Arts and Sciences: Mathematical Sciences

DNA microarray is a widely used high-throughput technology to measure the expression level of tens of thousands of genes simultaneously. With increasing availability of microarray genomics data, various clustering algorithms have been explored to identify the latent patterns in gene expression data as well as discover disease subtypes. Interesting connections that can be founded correlating differential-expressed genes evidence to other biological information are very important in developing a full picture of the biological pathways as well as in giving insightful suggestions to the new conducted experiments. The abundant biological information we need to identify the disease signature is organized in the functional categories. Thus, relating the microarray experiments to the functional categories could lead to a better understanding of the underlying biological process and help develop targeted treatment to a specific disease. In this dissertation, we investigated several Dirichlet process mixture (DPM) model based clustering methods that explicitly account for interactions across the functional category enrichment scores for improved sample clustering. Our clustering method represents microarray data enrichment score profiles as multivariate Gaussian random variables with structured or unstructured correlation. Also we demonstrate by a simulation study that when correlation exist, our algorithm will outperform the other clustering algorithm assume independence. Furthermore, factor analysis based clustering procedure is developed to search for the correct underlying correlation pattern and we optimize the number of factors using the Metropolised Carlin and Chib method based model selection algorithm. In such a way, we reduce the number of parameters to be estimated in the unstructured covariance matrix model and also incorporate the unknown variance-covariance structure across different functional categories. The main contributions of our ap (open full item for complete abstract)

Committee: Siva Sivaganesan Ph.D. (Committee Chair); Seongho Song Ph.D. (Committee Member); Xia Wang Ph.D. (Committee Member) Subjects: Statistics

PhD, University of Cincinnati, 2008, Medicine : Toxicology (Environmental Health)

The white rot fungus Phanerochaete chrysosporiumis primarily known for its ability to degrade a wide range of xenobiotic compounds including the highly recalcitrant polycyclic aromatic hydrocarbons. The natural substrate of this basidiomycete fungus is however, lignin, the most abundant aromatic polymer on earth. The versatililty of this fungus in breaking down a wide array of compounds arises from the presence of a highly nonspecific enzyme system (peroxidase enzyme system) in its repertoire. Most of the research involving degradation of toxic chemicals has focused on this biodegrading enzyme machinery. Cytochrome P450 monooxygenases (P450s) on the other hand, are heme-thiolate proteins that are known to be involved in metabolism of endogenous compounds as well as xenobiotic compounds in higher eukaryotes. Nearly 150 P450s are present in this organism, which is the highest number known till date among fungal species. Based on the sequence similarity criteria and our phylogenetic analysis, these P450s have been classified under 12 families and 23 sub-families. Despite indirect evidences suggesting the role of P450s in oxidation of xenobiotics, there have been hardly any reports on characterization and role of individual P450s either in regulation of physiological processes or in direct metabolism of xenobiotics in this organism. Here we characterized and investigated the role of P450 enzymes in two different mechanisms in this fungus. One, indirect involvement of P450s in peroxidase–mediated oxidation of xenobiotics, and two, direct involvement of P450s in metabolism of xenobiotics. In order to achieve the first objective, we investigated the role of PC-bphgene, the only member of the P450 CYP53 in synthesis of a secondary metabolite, veratryl alcohol, which regulates the activity of the peroxidase enzyme system of this fungus. In order to achieve the second objective, we used the functional genomic approach based on a custom-designed microarray and heterologous exp (open full item for complete abstract)

Committee: Dr. Jagjit Yadav (Advisor) Subjects:

Doctor of Philosophy, The Ohio State University, 2006, Electrical Engineering

The well-known Linear Discriminant Analysis (LDA) approach to feature extraction in classification problems is typically formulated using a generalized eigenvalue decomposition, S 1V=S 2VΛ, where S 1and S 2are two symmetric, positive-semidefinite matrices defining the meature to be maximized and that to be minimized. Most of the LDA algorithms developed to date are based on tuning one of these two matrices to solve a specific problem. However, the search for a set of metrices that can be applied to a large number of problems has met difficulty. In this thesis, we take the view that most of these problems are caused by the use of the generalized eigenvalue decomposition equation described above. Further,we augue that many of these problems can be solved by studying and modifying this basic equation. At the core of this thesis lays a new factorization of S 2 -1S 1that can be used to resolve several of the problems of LDA. Three novel algorithms are derived, each based on our proposed factorization. In the first algorithm, we define a criterion to prune noisy bases in LDA. This is possible thanks to the flexibility of our factorization, which allows the suppression of a set of vectors of any metric. The second algorithm is called Subclass Discriminant Analysis (SDA). SDA can be applied to a large variety of distribution types because it approximates the underlying distribution of each class with a mixture of Gaussians. The most convenient number of Gaussians can be readily selected thanks to our proposed factorization. The third algorithm is aimed to address the over-fitting issue in LDA. A direct application of this algorithm is tumor classification, where the ratio of samples versus features is very small. The main idea of the proposed algorithm is to take advantage of the information embedded in the testing samples - changing the role of testing data from passive to active samples. In all three cases, extensive experimental results are provided using a large variety (open full item for complete abstract)

Committee: Aleix Martinez (Advisor) Subjects:

Doctor of Philosophy, The Ohio State University, 2005, Statistics

Microarray, a bio-technology that allows monitoring of gene expressions for thousands of genes simultaneously, has revolutionized biological and genomic research and holds promising potentials in many real applications, such as drug targeting, gene profiling, disease diagnosis and prognosis, pharmacogenomics, etc. Along with its unprecedented potential, microarray technology presents miscellaneous challenges in statistical analysis of microarray gene expression data. Many sources of extraneous variations are present in a microarray experiment. Adjusting these extraneous variations is critical to the separation of biological signals from artifacts. Moreover, microarray gene expression data typically are of extremely large dimension, consisting of tens of thousands of observations. Computational efficiency in statistical analysis is therefore crucial. For testing the significance of biological signal, multiplicity adjustment is indispensable. We propose a modeling approach that allows flexible experimental design, while providing accurate estimation and easy multiplicity-adjusted inferences. This modeling approach is suitable for various types of microarrays, including both cDNA and oligonucleotide microarrays. The statistical modeling and multiplicity-adjusted inference are integrated into an R package, MultiArray , as a computationally efficient environment. Real microarray experiment examples show that our modeling approach and MultiArray outperform other popular packages in both detecting differences and establishing equivalence in gene expressions.

Committee: Jason Hsu (Advisor) Subjects: Statistics

Doctor of Philosophy, The Ohio State University, 2004, Pathology

Monocytes are precursors to tissue macrophages. We performed microarray expression analysis to determine the genetic expression profiles of peripheral blood monocytes (PBM) and alveolar macrophages (AM). Our data indicates that several hundred genes are differentially regulated in PBM and AM. These include genes involved in cellular scavenging, intracellular signaling pathways, cellular survival and/or differentiation. We observed that the chemokine receptor expression profiles of PBM and AM differed in the gene array analysis, and confirmed these results by reverse transcriptase polymerase chain reaction, flow cytometry and functional analyses. Our data indicates that circulating monocytes express the chemokine receptors CCR1 and CCR2, and that monocytes functionally respond by migrating toward both MCP-1 and MIP-1a. In contrast, alveolar macrophages do not express CCR1 or CCR2, but do express the MIP-1a chemokine receptor CCR5. AM did not respond to MCP-1 but did respond to MIP-1a in a migration assay. The addition of an anti-CCR5 blocking antibody completely abrogated MIP-1a-induced migration in AM, but did not affect monocytes. These data may be helpful in understanding the regulated recruitment of inflammatory cells in areas of lung inflammation. This data is relevant to human disease, as in pulmonary fibrosis (PF) high concentrations of MCP-1 are found in the lung lavage fluid from affected patients but not in normal volunteers. PF is a serious lung disease characterized by progressive scarring of the lung tissue, eventually leading to hypoxemia and death. Prognosis is worsened in patients with more monocytes and macrophages in their lungs. We used an animal model of bleomycin-induced PF to examine the role of Macrophage Colony-Stimulating Factor (M-CSF) in the development of this disease. We chose this model because mice that are genetically deficient in M-CSF have decreased numbers of circulating monocytes and tissue macrophages, and provide a useful model f (open full item for complete abstract)

Committee: Clay Marsh (Advisor) Subjects:

Doctor of Philosophy, The Ohio State University, 2003, Neuroscience

Our concept of multiple sclerosis (MS) has expanded from an inflammatory demyelinating disease of the central nervous system (CNS) to one incorporating the concept of MS having an inflammatory mediated neurodegenerative component causing extensive neuronal damage resulting in permanent neurological disability. The majority (80-90%) of typical MS patients have a relapsing-remitting (RR) disease course. Early in the disease, RR-MS patients often benefit from anti-inflammatory therapeutics and recover neurological function with resolution of inflammation and edema. Eventually, many patients enter the secondary progressive phase of the disease characterized by increasing neurological disability, minimal recovery and poor efficacy of anti-inflammatory therapeutics. Why is the progression of RR-MS biphasic? Demyelination and inflammation certainly cause reversible neurological disability and may cause permanent neurological disability in MS. However, neurological decline doesn't always associate with inflammatory lesions in MS and recent evidence indicates chronically demyelinated axons are capable of conducting signals suggesting irreversible neurological disability is due to additional mechanisms. We identified significant amounts of axonal transection in WM lesions from MS patients with short disease duration, indicating axonal transection occurs from disease onset. Additionally, we identified numerous cortical lesions in MS brains containing extensive neuronal pathology characterized by transected axons, transected dendrites, and apoptotic neurons. Widespread cortical pathology in MS brains identifies another component in the physiopathogenesis of MS and further implicates neuronal pathology in the development of irreversible neurological disabilities. We along with others suggest recovery early in the disease from neurological disability is due to resolution of inflammation and edema, remyelination, and the capacity of the CNS to compensate for neuronal injury. Event (open full item for complete abstract)

Committee: Michael Beattie (Advisor) Subjects:

Doctor of Philosophy, Case Western Reserve University, 2011, EECS - Computer and Information Sciences

Medical prognostic models are designed by clinicians to predict the future course or outcome of disease progression after diagnosis or treatment. The data, which are used when these clinical models are developed, are required to contain a high number of events per variable (EPV) for the resulting model to be reliable. If our objective is to optimize predictive performance by some criterion, we can often achieve a reduced model that has a little bias with low variance, but whose overall performance is improved. To accomplish this goal, we propose a new variable selection approach that combines Stepwise Tuning in the Maximum Concordance Index (STMC) and Forward Nested Subset Selection (FNSS) in two stages. In the first stage, the proposed variable selection is employed to identify the best subset of risk factors optimized with the concordance index using inner cross validation for optimism correction in the outer loop of cross validation, yielding potentially different final models for each of the folds. We then feed the intermediate results of the prior stage into another selection method in the second stage to resolve the overfitting problem and to select a final model from the variation of predictors in the selected models. Two case studies on relatively different sized survival data sets as well as a simulation study demonstrate that the proposed approach is able to select an improved and reduced average model under a sufficient sample and event size compared to other selection methods such as stepwise selection using the likelihood ratio test, Akaike Information Criterion (AIC), and least absolute shrinkage and selection operator (lasso). Finally, we achieve improved final models in each dataset as compared full models according to most criteria. These results of the model selection models and the final models were analyzed in a systematic scheme through validation for independent performance evaluation. For the second part of this dissertation, we build prognos (open full item for complete abstract)

Committee: Michael Kattan (Advisor); Mehmet Koyuturk (Committee Chair); Andy Podgurski (Committee Member); Soumya Ray (Committee Member) Subjects: Computer Science

Doctor of Philosophy, Case Western Reserve University, 2008, Statistics

Statistical data mining is one of the most active research areas. In this thesis we develop two new data mining procedures and explore their applications to genetic data. The first procedure is called PfCluster - Profile Cluster Analysis. It is a clustering method designed for profiled genetic data. The PfCluster is efficient and flexible in uncovering clusters determined by a new class of biologically meaningful distance metrics. A new internal quality measure of clusters, coherence index, is developed to find coherent clusters. An efficient mechanism for choosing the threshold of coherent clusters is also derived and implemented. The threshold is based on the first and second order approximations to the true threshold under a null distribution for parallel clusters. The PfCluster has been applied to simulated data and two real data examples: a biomarker LOH dataset and a microarray gene expression dataset. PfCluster is competitive to the correlation-based clustering procedures. The second procedure is called RPselection - Resampling based partitioning selection. It is a feature selection algorithm designed for microarray studies. It selects a subset of genes that maximizes a fitness score. The fitness score measures the relevance between the partition labels from a clustering result and an external class label derived from the clinical outcomes. The score is computed using a resampling procedure. The RPselection algorithm has been applied to simulated data and a real uveal melanoma gene expression data. RPselection outperforms gene-by-gene test-based feature selection procedures. Software development is an integral part of modern statistical research. Two software packages, pfclust and rpselect, are developed in this thesis based on our PfCluster method and RPselection algorithm. Packages pfclust and rpselect are implemented based on R object-oriented programming framework, and they can be easily customized and extended by users. The ideas in our two procedures ca (open full item for complete abstract)

Committee: Jiayang Sun (Advisor) Subjects: Statistics

Doctor of Philosophy (Ph.D.), Bowling Green State University, 2010, Biological Sciences

Shoot apical meristems (SAMs) of higher plants maintain a population of pluripotent cells which continue to divide throughout the life of plant and provide cells for development of all the above-ground organs after embryogenesis. Since the plant is sessile, maintenance of stem cells in the SAM and appropriate differentiation are crucial for the plants to adapt to the changing environments. Shoot apex of the plant is the very tip of the shoot, in which the SAM resides. To analyze the differential gene expression patterns between the shoot apex and the very young leaf, transcriptomes from the two tissue types of a variegated variety of English ivy (Hedera helix L. cv. Goldheart) plants, were hybridized on Arabidopsis thaliana cDNA microarrays, using cross-species hybridization (CSH). Among 11,255 cDNA probes excluding ‘BLANK' and ‘bad' spots, 2,597 features produced signals that were greater than background levels, which constitutes 23% of the total number of usable probes on the microarray. One hundred seventy four genes were expressed statistically differentially (fold change >=2 and p=0.05). Of these, 60 were in the shoot apex and 114 were in the young leaf. Functional categorization based on the genome/protein databases and a pathway analysis revealed some of the tissue-specific biological processes and identified some of the genes involved. The annotated and/or predicted roles of those genes in the tissue-specific biological processes were described and discussed in relation to the plant development.

Committee: Scott O. Rogers PhD (Advisor); Kit C. Chan PhD (Committee Chair); Carmen F. Fioravanti PhD (Committee Member); Helen Michaels PhD (Committee Member); Paul F. Morris PhD (Committee Member) Subjects: Bioinformatics