Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^

The potential role of environmental exposures and genomic signaling in development of central nervous system tumors

The etiology of central nervous system tumors (CNSTs) is mainly unknown. Aside from extremely rare genetic conditions, such as neurofibromatosis and tuberous sclerosis, the only unequivocally identified risk factor is exposure to ionizing radiation, and this explains only a very small fraction of cases. Using meta-analysis, gene networking and bioinformatics methods, this dissertation explored the hypothesis that environmental exposures produce genetic and epigenetic alterations that may be involved in the etiology of CNSTs. A meta-analysis of epidemiological studies of pesticides and pediatric brain tumors revealed a significantly increased risk of brain tumors among children whose mothers had farm-related exposures during pregnancy. A dose response was recognized when this risk estimate was compared to those for risk of brain tumors from maternal exposure to non-agricultural pesticides during pregnancy, and risk of brain tumors among children exposed to agricultural activities. Through meta-analysis of several microarray studies which compared normal tissue to astrocytomas, we were able to identify a list of 554 genes which were differentially expressed in the majority of astrocytomas. Many of these genes have in fact been implicated in development of astrocytoma, including EGFR, HIF-1α, c-Myc, WNT5A, and IDH3A. Reverse engineering of these 554 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I-IV), and ‘key genes’ within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme (GBM) were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. Lastly, bioinformatics analysis of environmental databases and curated published results on GBM was able to identify numerous potential pathways and geneenvironment interactions that may play key roles in astrocytoma development. Findings from this research have strong potential to advance our understanding of the etiology and susceptibility to CNSTs. Validation of our ‘key genes’ and pathways could potentially lead to useful tools for early detection and novel therapeutic options for these tumors.

Carbon nanostructure based electrodes for high efficiency dye sensitized solar cell

Synthesis and functionalization of large-area graphene and its structural, electrical and electrochemical properties has been investigated. First, the graphene films, grown by thermal chemical vapor deposition (CVD), contain three to five atomic layers of graphene, as confirmed by Raman spectroscopy and high-resolution transmission electron microscopy. Furthermore, the graphene film is treated with CF4 reactive-ion plasma to dope fluorine ions into graphene lattice as confirmed by X-ray photoelectron spectroscopy (XPS) and UV-photoemission spectroscopy (UPS). Electrochemical characterization reveals that the catalytic activity of graphene for iodine reduction enhanced with increasing plasma treatment time, which is attributed to increase in catalytic sites of graphene for charge transfer. The fluorinated graphene is characterized as a counter-electrode (CE) in a dye-sensitized solar cell (DSSC) which shows ~ 2.56% photon to electron conversion efficiency with ~11 mAcm−2 current density. Second, the large scale graphene film is covalently functionalized with HNO3 for high efficiency electro-catalytic electrode for DSSC. The XPS and UPS confirm the covalent attachment of C-OH, C(O)OH and NO3- moieties with carbon atoms through sp2-sp3 hybridization and Fermi level shift of graphene occurs under different doping concentrations, respectively. Finally, CoS-implanted graphene (G-CoS) film was prepared using CVD followed by SILAR method. The G-CoS electro-catalytic electrodes are characterized in a DSSC CE and is found to be highly electro-catalytic towards iodine reduction with low charge transfer resistance (Rct ~5.05 Ωcm 2) and high exchange current density (J0~2.50 mAcm -2). The improved performance compared to the pristine graphene is attributed to the increased number of active catalytic sites of G-CoS and highly conducting path of graphene. We also studied the synthesis and characterization of graphene-carbon nanotube (CNT) hybrid film consisting of graphene supported by vertical CNTs on a Si substrate. The hybrid film is inverted and transferred to flexible substrates for its application in flexible electronics, demonstrating a distinguishable variation of electrical conductivity for both tension and compression. Furthermore, both turn-on field and total emission current was found to depend strongly on the bending radius of the film and were found to vary in ranges of 0.8 - 3.1 V/μm and 4.2 - 0.4 mA, respectively.