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. ^

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.