DNA methylation is an epigenetic mechanism that usually occurs at CpG sites in the genome. Both sequencing and array-based techniques are available to detect methylation patterns. Whole-genome bisulfite sequencing is the most comprehensive but cost-prohibitive approach, and microarrays represent an affordable alternative approach. Array-based methods are generally cheaper but assess a specific number of genomic loci, such as Illumina methylation arrays. Differentially methylated regions (DMRs) are genomic regions with specific methylation patterns across multiple CpG sites that associate with a phenotype. Methylation at nearby sites tends to be correlated, therefore it may be more powerful to study sets of sites to detect methylation differences as well as reduce the multiple testing burden, compared to utilizing individual sites. Several statistical approaches exist for identifying DMRs, and a few prior publications compared the performance of several commonly used DMR methods. However, as far as we know, no comprehensive comparisons have been made based on genome-wide simulation studies. This dissertation provides some comprehensive suggestions for DMR analysis based on genome-wide evaluations of existing DMR tools and presents the development of a novel approach to increase the power to identify DMRs with clinical value in genomic research. The second chapter presents genome-wide null simulations to compare five commonly used array-based DMR methods (Bumphunter, comb-p, DMRcate, mCSEA and coMethDMR) and identifies coMethDMR as the only approach that consistently yields appropriate Type I error control. We suggest that a genome-wide evaluation of false positive (FP) rates is critical for DMR methods. The third chapter develops a novel Principal Component Analysis based DMR method (denoted as DMRPC), which demonstrates its ability to identify DMRs using genome-wide methylation arrays with well-controlled FP rates at the level of 0.05. Compared to coMethDMR, DMRPC is a robust and powerful novel DMR tool that can examine more genomic regions and extract signals from low-correlation regions. The fourth chapter applies the new DMR approach DMRPC in two “real-world” datasets and identifies novel DMRs that are associated with several inflammatory markers.

Because of the vast data volume of hyperspectral imagery, compression becomes a necessary process for hyperspectral data transmission, storage, and analysis. Three-dimensional discrete wavelet transform (DWT) based algorithms are particularly of interest due to their excellent rate-distortion performance. This thesis investigates several issues surrounding efficient compression using JPEG2000. Firstly, the rate-distortion performance is studied when Principal Component Analysis (PCA) replaces DWT for spectral decorrelation with the focus on the use of a subset of principal components (PCs) rather than all the PCs. Secondly, the algorithms are evaluated in terms of data analysis performance, such as anomaly detection and linear unmixing, which is directly related to the useful information preserved. Thirdly, the performance of compressing radiance and reflectance data with or without bad band removal is compared, and instructive suggestions are provided for practical applications. Finally, low-complexity PCA algorithms are presented to reduce the computational complexity and facilitate the future hardware design.

Abiotic stresses cause extensive loss to agriculture production worldwide. Cowpea is an important legume crop grown widely in tropical and subtropical regions where high temperature, ultraviolet-B (UVB) radiation and drought are the common stress factors limiting production. Various vegetative, physiological, biochemical and reproductive plant attributes were assessed under a range of UVB radiation levels in Experiment I and in a combination with two doses of each carbon dioxide concentration [CO2], temperature, and UVB radiation and their interactions in Experiment II by using six cowpea genotypes and sunlit plant growth chambers. The dynamics of photosynthesis and fluorescence processes were assessed in 15 cowpea genotypes under drought condition in Experiment III in pot-grown plants under sunlit conditions. A distinct response pattern was not observed in cowpea in response to UVB radiation form 0 to 15 kJ; however, plants grown under elevated UVB showed reduced photosynthesis resulting in shorter plants and produced smaller flowers and lower seed yield. Increased phenolic compounds appeared to be a defense response to UVB radiation. The growth enhancements observed by doubling of [CO2] were not observed when plants were grown in combination with elevated UVB or temperature which also showed the most detrimental effects on plant growth and seed yield. Results form Experiment I and II revealed that cowpea reproductive traits were highly sensitive to abiotic stresses compared to the vegetative growth and development. A total stress response index (TSRI) technique, derived from all vegetative and reproductive parameters, was used to screen genotypes for their stress tolerance to UVB or combination of stresses. An increase in water use efficiency while maintaining higher rate of photosynthesis was an important drought tolerance mechanism in tolerant cowpea genotypes. Using principal component analysis technique, four groups of the genotypes were identified for their drought tolerance. Evaluating same genotypes across stress conditions revealed that no single genotype has the absolute tolerance characters to all stress conditions. The identified diversity for abiotic stress tolerance among cowpea genotypes and associated traits can be used to develop tolerant genotypes suitable for an agro-ecological niche though traditional breeding or genetic engineering methods.

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