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>Magister Scientiae - MSc / A large volume of gene expression data exists in public repositories like the NCBI’s Gene Expression Omnibus (GEO) and the EBI’s ArrayExpress and a significant opportunity to re-use data in various combinations for novel in-silico analyses that would otherwise be too costly to perform or for which the equivalent sample numbers would be difficult to collects exists. For example, combining and re-analysing large numbers of data sets from the same cancer type would increase statistical power, while the effects of individual study-specific variability is weakened, which would result in more reliable gene expression signatures. Similarly, as the number of normal control samples associated with various cancer datasets are often limiting, datasets can be combined to establish a reliable baseline for accurate differential expression analysis. However, combining different microarray studies is hampered by the fact that different studies use different analysis techniques, microarray platforms and experimental protocols. We have developed and optimised a method which transforms gene expression measurements from continuous to discrete data points by grouping similarly expressed genes into quantiles on a per-sample basis. After cross mapping each probe on each chip to the gene it represents, thereby enabling us to integrate experiments based on genes they have in common across different platforms. We optimised the quantile discretization method on previously published prostate cancer datasets produced on two different array technologies and then applied it to a larger breast cancer dataset of 411 samples from 8 microarray platforms. Statistical analysis of the breast cancer datasets identified 1371 differentially expressed genes. Cluster, gene set enrichment and pathway analysis identified functional groups that were previously described in breast cancer and we also identified a novel module of genes encoding ribosomal proteins that have not been previously reported, but whose overall functions have been implicated in cancer development and progression. The former indicates that our integration method does not destroy the statistical signal in the original data, while the latter is strong evidence that the increased sample size increases the chances of finding novel gene expression signatures. Such signatures are also robust to inter-population variation, and show promise for translational applications like tumour grading, disease subtype classification, informing treatment selection and molecular prognostics.

This thesis proposes a new way to analyze facial expressions through 3D scanned faces of real-life people. The expression analysis is based on learning the facial motion vectors that are the differences between a neutral face and a face with an expression. There are several expression analysis based on real-life face database such as 2D image-based Cohn-Kanade AU-Coded Facial Expression Database and Binghamton University 3D Facial Expression Database. To handle large pose variations and increase the general understanding of facial behavior, 2D image-based expression database is not enough. The Binghamton University 3D Facial Expression Database is mainly used for facial expression recognition and it is difficult to compare, resolve, and extend the problems related detailed 3D facial expression analysis. Our work aims to find a new and an intuitively way of visualizing the detailed point by point movements of 3D face model for a facial expression. In our work, we have created our own 3D facial expression database on a detailed level, which each expression model has been processed to have the same structure to compare differences between different people for a given expression. The first step is to obtain same structured but individually shaped face models. All the head models are recreated by deforming a generic model to adapt a laser-scanned individualized face shape in both coarse level and fine level. We repeat this recreation method on different human subjects to establish a database. The second step is expression cloning. The motion vectors are obtained by subtracting two head models with/without expression. The extracted facial motion vectors are applied onto a different human subject’s neutral face. Facial expression cloning is proved to be robust and fast as well as easy to use. The last step is about analyzing the facial motion vectors obtained from the second step. First we transferred several human subjects’ expressions on a single human neutral face. Then the analysis is done to compare different expression pairs in two main regions: the whole face surface analysis and facial muscle analysis. Through our work where smiling has been chosen for the experiment, we find our approach to analysis through face scanning a good way to visualize how differently people move their facial muscles for the same expression. People smile in a similar manner moving their mouths and cheeks in similar orientations, but each person shows her/his own unique way of moving. The difference between individual smiles is the differences of movements they make.

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<p> Sinorhizobium meliloti is an alpha-proteobacterium that forms symbiotic nodules on the roots of Medicago sativa (alfalfa). The ability to catabolize specific compounds available in the soil is one of the best-characterized factors to increase competition for nodulation. In order to successfully attain symbiosis S. meliloti must compete for nutrients in the rhizosphere, which can be done by having a large number of transport systems encoded in its genome. Genes encoding proteins involved in transport constitute the largest (12%) class of genes in the S. meliloti genome. Great interest now lies in determining substrates for the transport systems and their role in the survival and fitness of S. meliloti.</p> <p> An estimated 824 transport genes in the genome of the soil bacterium Sinorhizobium meliloti are predicted to encode 382 transport systems. All of the S. meliloti transporters had been studied under 120 different conditions, including growth on various carbon and nitrogen sources, seed and root exudates and starvation conditions.</p> <p> From this screen of every transport system in S. meliloti, the substrates that induce expression of over 50 transport systems have been identified. We have found putative transporters for amino acids, sugars, sugar alcohols, amino sugars, betaines and other compounds that might be found in the soil. This large scale expression analysis gives insight into the natural environment of S. meliloti by studying those genes that are induced by compounds that would be found in the soil.</p> / Thesis / Master of Science (MSc)

Recent advances in high-throughput technique enables the generation of a large amount of omic data such as genomics, transcriptomics, proteomics, metabolomics, glycomics etc. Typically, differential expression analysis (e.g., student's t-test, ANOVA) is performed to identify biomolecules (e.g., genes, proteins, metabolites, glycans) with significant changes on individual level between biologically disparate groups (disease cases vs. healthy controls) for cancer biomarker discovery. However, differential expression analysis on independent studies for the same clinical types of patients often led to different sets of significant biomolecules and had only few in common. This may be attributed to the fact that biomolecules are members of strongly intertwined biological pathways and highly interactive with each other. Without considering these interactions, differential expression analysis could lead to biased results. Network-based methods provide a natural framework to study the interactions between biomolecules. Commonly used data-driven network models include relevance network, Bayesian network and Gaussian graphical models. In addition to data-driven network models, there are many publicly available databases such as STRING, KEGG, Reactome, and ConsensusPathDB, where one can extract various types of interactions to build knowledge-driven networks. While both data- and knowledge-driven networks have their pros and cons, an appropriate approach to incorporate the prior biological knowledge from publicly available databases into data-driven network model is desirable for more robust and biologically relevant network reconstruction. Recently, there has been a growing interest in differential network analysis, where the connection in the network represents a statistically significant change in the pairwise interaction between two biomolecules in different groups. From the rewiring interactions shown in differential networks, biomolecules that have strongly altered connectivity between distinct biological groups can be identified. These biomolecules might play an important role in the disease under study. In fact, differential expression and differential network analyses investigate omic data from two complementary perspectives: the former focuses on the change in individual biomolecule level between different groups while the latter concentrates on the change in pairwise biomolecules level. Therefore, an approach that can integrate differential expression and differential network analyses is likely to discover more reliable and powerful biomarkers. To achieve these goals, we start by proposing a novel data-driven network model (i.e., LOPC) to reconstruct sparse biological networks. The sparse networks only contains direct interactions between biomolecules which can help researchers to focus on the more informative connections. Then we propose a novel method (i.e., dwgLASSO) to incorporate prior biological knowledge into data-driven network model to build biologically relevant networks. Differential network analysis is applied based on the networks constructed for biologically disparate groups to identify cancer biomarker candidates. Finally, we propose a novel network-based approach (i.e., INDEED) to integrate differential expression and differential network analyses to identify more reliable and powerful cancer biomarker candidates. INDEED is further expanded as INDEED-M to utilize omic data at different levels of human biological system (e.g., transcriptomics, proteomics, metabolomics), which we believe is promising to increase our understanding of cancer. Matlab and R packages for the proposed methods are developed and available at Github (https://github.com/Hurricaner1989) to share with the research community. / Ph. D. / High-throughput technique such as transcriptomics, proteomics and metabolomics is widely used to generate ‘big’ data for cancer biomarker discovery. Typically, differential expression analysis is performed to identify cancer biomarkers. However, discrepancies from independent studies for the same clinical types of samples using differential expression analysis are observed. This may be attributed to that biomolecules such as genes, proteins and metabolites are members of strongly intertwined biological pathways and highly interactive with each other. Without considering these interactions, differential expression analysis could lead to biased results. In this dissertation, we propose to identify cancer biomarker candidates using network-based approaches. We start by proposing a novel data-driven network model (i.e., LOPC) to reconstruct sparse biological networks. Then we propose a novel method (i.e., wgLASSO) to incorporate prior biological knowledge from public available databases into purely data-driven network model to build biologically relevant networks. In addition, a novel differential network analysis method (i.e., dwgLASSO) is proposed to identify cancer biomarkers. Finally, we propose a novel network-based approach (i.e., INDEED) to integrate differential expression and differential network analyses. INDEED is further expanded as INDEED-M to utilize omic data at different levels of human biological system (e.g., transcriptomics, proteomics, and metabolomics) to identify cancer biomarkers from a systems biology perspective. Matlab and R packages for the proposed methods are developed and shared with the research community.

The Breast cancer gene 1 (BRCA1) codes for nuclear phosphoprotein with a key function in the regulation of DNA damage response. The BRCA1 protein contributes to the formation and regulation of protein supercomplexes that participates on the DNA double-strand break repair. These protein supercomplexes are formed by the protein-protein interactions between highly conservative protein motives in BRCA1 and its binding partners. Except to the wild type form of BRCA1 mRNA containing entire set of 22 exons coding for the 220 kD protein, numerous alternative splicing variants (ASVs) BRCA1 mRNA has been described. These ASVs code for BRCA1 isoforms lacking several critical functional domains. It has been proposed, that formation of BRCA1's ASVs represent a tool for regulation of BRCA1 function. Only poorly has been characterized a complex catalogue of in various human tissues and their expression. This study aims to address these questions. We optimized the identification of BRCA1's ASVs including those covering the entire transcripts of the wt BRCA1 mRNA with length exceeding 5.5 kb. In further analysis, we characterized 13 BRCA1's ASVs in RNA samples isolated from peripheral blood mononuclear cells (PBMNC) obtained from patients with breast cancer (BC) and control subjects. The majority of the identified...

Background: Many questions in statistical genomics can be formulated in terms of variable selection of candidate biological factors for modeling a trait or quantity of interest. Often, in these applications, additional covariates describing clinical, demographical or experimental effects must be included a priori as mandatory covariates while allowing the selection of a large number of candidate or optional variables. As genomic studies routinely require mandatory covariates, it is of interest to propose principled methods of variable selection that can incorporate mandatory covariates. Methods: In this article, we propose the ridge-lasso hybrid estimator (ridle), a new penalized regression method that simultaneously estimates coefficients of mandatory covariates while allowing selection for others. The ridle provides a principled approach to mitigate effects of multicollinearity among the mandatory covariates and possible dependency between mandatory and optional variables. We provide detailed empirical and theoretical studies to evaluate our method. In addition, we develop an efficient algorithm for the ridle. Software, based on efficient Fortran code with R-language wrappers, is publicly and freely available at https://sites.google.com/site/zhongyindaye/software. Results: The ridle is useful when mandatory predictors are known to be significant due to prior knowledge or must be kept for additional analysis. Both theoretical and comprehensive simulation studies have shown that the ridle to be advantageous when mandatory covariates are correlated with the irrelevant optional predictors or are highly correlated among themselves. A microarray gene expression analysis of the histologic grades of breast cancer has identified 24 genes, in which 2 genes are selected only by the ridle among current methods and found to be associated with tumor grade. Conclusions: In this article, we proposed the ridle as a principled sparse regression method for the selection of optional variables while incorporating mandatory ones. Results suggest that the ridle is advantageous when mandatory covariates are correlated with the irrelevant optional predictors or are highly correlated among themselves.

The automotive industry is facing significant technological and sociological shifts, calling for an improved understanding of driver and passenger behaviours, emotions and needs, and a transformation of the traditional automotive design process. This research takes a human-centred approach to automotive research, investigating the users' emotional states during automobile driving, with the goal to develop a framework for automotive emotion research, thus enabling the integration of technological advances into the driving environment. A literature review of human emotion and emotion in an automotive context was conducted, followed by three driving studies investigating emotion through Facial-Expression Analysis (FEA): An exploratory study investigated whether emotion elicitation can be applied in driving simulators, and if FEA can detect the emotions triggered. The results allowed confidence in the applicability of emotion elicitation to a lab-based environment to trigger emotional responses, and FEA to detect those. An on-road driving study was conducted in a natural setting to investigate whether natures and frequencies of emotion events could be automatically measured. The possibility of assigning triggers to those was investigated. Overall, 730 emotion events were detected during a total driving time of 440 minutes, and event triggers were assigned to 92% of the emotion events. A similar second on-road study was conducted in a partially controlled setting on a planned road circuit. In 840 minutes, 1947 emotion events were measured, and triggers were successfully assigned to 94% of those. The differences in natures, frequencies and causes of emotions on different road types were investigated. Comparison of emotion events for different roads demonstrated substantial variances of natures, frequencies and triggers of emotions on different road types. The results showed that emotions play a significant role during automobile driving. The possibility of assigning triggers can be used to create a better understanding of causes of emotions in the automotive habitat. Both on-road studies were compared through statistical analysis to investigate influences of the different study settings. Certain conditions (e.g. driving setting, social interaction) showed significant influence on emotions during driving. This research establishes and validates a methodology for the study of emotions and their causes in the driving environment through which systems and factors causing positive and negative emotional effects can be identified. The methodology and results can be applied to design and research processes, allowing the identification of issues and opportunities in current automotive design to address challenges of future automotive design. Suggested future research includes the investigation of a wider variety of road types and situations, testing with different automobiles and the combination of multiple measurement techniques.

Coral reefs on Florida’s Reef Tract (FRT) are susceptible to many anthropogenic influences including controlled freshwater discharges and agricultural runoff as well as high natural environmental variability from seasonal rainfall, runoff and upwelling. To better understand coral population structure and responses to sublethal stressors, populations of the scleractinian coral Montastraea cavernosa in the northern FRT were examined using a combination of genomic and transcriptomic techniques. Microsatellite genetic markers identified high local retention among sites and a slight southward gene flow. An in-situ temporal gene expression analysis utilizing a tag-based sequencing transcriptomic approach was used to analyze baseline coral health at St. Lucie Reef (SLR), off Stuart, FL. Temporal variation had the greatest influence of differential gene expression among M. cavernosa at SLR. Results will be shared with local resource managers and coupled with a complementary ex-situ experimental trial. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection