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Dissertations / Theses on the topic 'Microarray analysis'

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Published: 4 June 2021

Last updated: 31 July 2024

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1

Wang, Tao. "Statistical design and analysis of microarray experiments." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1117201363.

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Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains ix, 146 p.; also includes graphics (some col.) Includes bibliographical references (p. 145-146). Available online via OhioLINK's ETD Center

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2

Stephens, Nathan Wallace. "A Comparison of Microarray Analyses: A Mixed Models Approach Versus the Significance Analysis of Microarrays." BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/1115.

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DNA microarrays are a relatively new technology for assessing the expression levels of thousands of genes simultaneously. Researchers hope to find genes that are differentially expressed by hybridizing cDNA from known treatment sources with various genes spotted on the microarrays. The large number of tests involved in analyzing microarrays has raised new questions in multiple testing. Several approaches for identifying differentially expressed genes have been proposed. This paper considers two: (1) a mixed models approach, and (2) the Signiffcance Analysis of Microarrays.

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3

Lau, Kelvin Ee Ming. "Microarray analysis of Acidovorax temperans." Thesis, University of Auckland, 2008. http://hdl.handle.net/2292/5869.

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Bacteria belonging to the genus Acidovorax have been shown to be a consistent member of the activated sludge microbial community. Two phenotypic variants of A. temperans CB2 isolated locally from activated sludge exhibit noteworthy characteristics, such as the ability to form biofilms and flocs, which are critical microbial processes underlying all modern wastewater treatment systems. Gene expression microarray technology is a functional genomics platform that enables the simultaneous interrogation of all expressed transcripts during normal cell ontogeny, or in response to specific environmental stimuli. Microarray technology offers the opportunity to investigate gene expression changes relevant to key processes in wastewater treatment, using A. temperans as a model organism. The aims of this research were to develop a full genome microarray platform for A. temperans CB2 and to use this microarray platform to investigate major differences in gene expression between the Hpos and Hneg phenotypic variants. An optimised gene expression microarray platform was established through the assessment of various experimental methods, such as RNA extraction, RNA amplification, microarray probe design, and quantitative PCR. Using the microarray platform, gene expression comparisons were obtained for planktonic broth cultures, static biofilms and bacterial colonies. Gene expression analyses have provided insights into the complex developmental processes involved in the transition from planktonic cells to stages of initial attachment, cell proliferation, biofilm maturation and nutrient limitation during the formation of A. temperans biofilms. Factors that have been identified in other bacterial systems such as type IV pili and activation of stress responses were also observed in A. temperans biofilms. In addition, several intriguing classes of genes, such as transcriptional regulators, a toxinantitoxin gene cassette, and nitrate metabolism were also found to be differentially expressed during the formation of A. temperans biofilm. The incorporation of microarray technology with other functional genomics techniques to investigate molecular mechanisms underlying the complex processes occurring in wastewater treatment will provide a scientific basis to improve the reliability of current wastewater treatment strategies and for the development of new treatment technologies.

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4

O'Neill, Paul. "Improved analysis of microarray images." Thesis, Brunel University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435755.

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5

Selvaraja, Sudarshan. "Microarray Data Analysis Tool (MAT)." University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1227467806.

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6

Stephens, Nathan W. "A comparison of genetic microarray analyses : a mixed models approach versus the significance analysis of microarrays /." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1604.pdf.

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7

Dvergsten, Erik C. "A Weighted Gene Co-expression Network Analysis for Streptococcus sanguinis Microarray Experiments." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4430.

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Streptococcus sanguinis is a gram-positive, non-motile bacterium native to human mouths. It is the primary cause of endocarditis and is also responsible for tooth decay. Two-component systems (TCSs) are commonly found in bacteria. In response to environmental signals, TCSs may regulate the expression of virulence factor genes. Gene co-expression networks are exploratory tools used to analyze system-level gene functionality. A gene co-expression network consists of gene expression profiles represented as nodes and gene connections, which occur if two genes are significantly co-expressed. An adjacency function transforms the similarity matrix containing co-expression similarities into the adjacency matrix containing connection strengths. Gene modules were determined from the connection strengths, and various network connectivity measures were calculated. S. sanguinis gene expression profile data was loaded for 2272 genes and 14 samples with 3 replicates each. The soft thresholding power β=6 was chosen to maximize R2 while maintaining a high mean number of connections. Nine modules were found. Possible meta-modules were found to be: Module 1: Blue & Green, Module 2: Pink, Module 3: Yellow, Brown & Red, Module 4: Black, Module 5: Magenta & Turquoise. The absolute value of module membership was found to be highly positively correlated with intramodular connectivity. Each of the nine modules were examined. Two methods (intramodular connectivity and TOM-based connectivity followed by network mapping) for identifying candidate hub genes were performed. Most modules provided similar results between the two methods. Similar rankings between the two methods can be considered equivalent and both can be used to detect candidate hub genes. Gene ontology information was unavailable to help select a module of interest. This network analysis would help researchers create new research hypotheses and design experiments for validation of candidate hub genes in biologically important modules.

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8

Fellenberg, Kurt. "Storage and analysis of microarray data." [S.l.] : [s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=964718839.

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9

Hare, Brian K. Dinakarpandian Deendayal. "Feature selection in DNA microarray analysis." Diss., UMK access, 2004.

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Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2004.
"A thesis in computer science." Typescript. Advisor: D. Dinakarpandian. Vita. Title from "catalog record" of the print edition Description based on contents viewed Feb. 24, 2006. Includes bibliographical references (leaves 81-86 ). Online version of the print edition.

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10

Hultin, Emilie. "Genetic Sequence Analysis by Microarray Technology." Doctoral thesis, Stockholm : School of Biotechnology, Royal Institute of Technology, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4330.

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11

Eijsden, Rudy Gerardus Elisabeth van. "Microarray analysis of oxidative phosphorylation disorders." [Maastricht] : Maastricht : Maastricht University ; University Library, Universiteit Maastricht [host], 2008. http://arno.unimaas.nl/show.cgi?fid=10708.

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12

Ronksley, Jonathan N. "Microarray Analysis of P19CL6 Cardiac Differentiation." Thesis, University of Nottingham, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.518881.

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13

Huang, Liping. "STATISTICAL METHODS IN MICROARRAY DATA ANALYSIS." UKnowledge, 2009. http://uknowledge.uky.edu/gradschool_diss/795.

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This dissertation includes three topics. First topic: Regularized estimation in the AFT model with high dimensional covariates. Second topic: A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data. Third topic: Normalization and analysis of cDNA microarray using linear contrasts.

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Brandt, Regine, Robert Grützmann, Andrea Bauer, Ralf Jesenofsky, Jörg Ringel, Matthias Löhr, Christian Pilarsky, and Jörg D. Hoheisel. "DNA microarray analysis of pancreatic malignancies." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-136556.

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Pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis. To improve the prognosis, novel molecular markers and targets for earlier diagnosis and adjuvant and/or neoadjuvant treatment are needed. Recent advances in human genome research and high-throughput molecular technologies make it possible to cope with the molecular complexity of malignant tumors. With DNA array technology, mRNA expression levels of thousand of genes can be measured simultaneously in a single assay. As several studies using microarrays in PDAC have already been published, this review attempts to compare the published data and therefore to validate the results. In addition, the applied techniques are discussed in the context of pancreatic malignancies
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich

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15

Jen, Chih-Hung. "Microarray data analysis for Arabidopsis thaliana." Thesis, University of Leeds, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417754.

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Rogers, Simon David. "Machine learning techniques for microarray analysis." Thesis, University of Bristol, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.409426.

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17

Pearson, Richard Peter. "Developing and benchmarking microarray analysis methods." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549322.

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18

Amaral, Telmo. "Analysis of breast tissue microarray spots." Thesis, University of Dundee, 2010. https://discovery.dundee.ac.uk/en/studentTheses/0a83915d-2f11-4b89-9c24-8dc3c15346f2.

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Tissue microarrays (TMAs) are a high-throughput technique that facilitates the survey of very large numbers of tumours, important both in clinical and research applications. However, the assessment of stained TMA sections is laborious and still needs to be carried manually, constituting a bottleneck in the pathologist?s work-flow. This process is also prone to perceptual errors and observer variability.Thus, there is strong motivation for the development of automated quantitative analysis of TMA image data. The analysis of breast TMA sections subjected to nuclear immunostaining begins with the classification of each spot as to the maintype of tissue that it contains, namely tumour, normal, stroma, or fat. Tumour and normal spots are then assigned a so-called quickscore composed of a pair or integer values, the first reflecting the proportion of epithelial nuclei that are stained, and the second reflecting the strength of staining of those nuclei. In this work, an approach was developed to analyse breast TMA spots subjectedto progesterone receptor immunohistochemistry. Spots were classified into their four main types through a method that combined a bag of features approachand classifiers based on either multi-layer perceptrons or latent Dirichlet allocation models. A classification accuracy of 74.6 % was achieved. Tumour and normal spots were scored via an approach that involved the computation of global features formalising the quickscore values used by pathologists, and the use of Gaussian processes for ordinal regression to predict actual quickscores based on global features. Mean absolute errors of 0.888 and 0.779 were achieved in the prediction of the first and second quickscore values, respectively. By setting thresholds on prediction confidence, it was possible to classify and score fractions of spots with substantially higher accuracies and lower mean absolute errors. Amethod for the segmentation of TMA spots into regions of different types was also investigated, to explore the generative nature of latent Dirichlet allocation models.

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19

Brandt, Regine, Robert Grützmann, Andrea Bauer, Ralf Jesenofsky, Jörg Ringel, Matthias Löhr, Christian Pilarsky, and Jörg D. Hoheisel. "DNA microarray analysis of pancreatic malignancies." Karger, 2004. https://tud.qucosa.de/id/qucosa%3A27711.

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Pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis. To improve the prognosis, novel molecular markers and targets for earlier diagnosis and adjuvant and/or neoadjuvant treatment are needed. Recent advances in human genome research and high-throughput molecular technologies make it possible to cope with the molecular complexity of malignant tumors. With DNA array technology, mRNA expression levels of thousand of genes can be measured simultaneously in a single assay. As several studies using microarrays in PDAC have already been published, this review attempts to compare the published data and therefore to validate the results. In addition, the applied techniques are discussed in the context of pancreatic malignancies.
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

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20

Teixeira, Bellina Ribau. "Computational methods for microarray data analysis." Master's thesis, Universidade de Aveiro, 2009. http://hdl.handle.net/10773/3989.

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Mestrado em Engenharia de Computadores e Telemática
Os microarrays de ácido desoxirribonucleico (ADN) são uma importante tecnologia para a análise de expressão genética. Permitem medir o nível de expressão de genes em várias amostras para, por exemplo, identificar genes cuja expressão varia com a administração de determinado medicamento. Um slide de microarray mede o nível de expressão de milhares de genes numa amostra ao mesmo tempo e uma experiência pode usar vários slides, surgindo assim muitos dados que é preciso processar e analisar, com recurso a meios informáticos. Esta dissertação inclui um levantamento de métodos e recursos de software utilizados na análise de dados de experiências de microarrays. Em seguida, descreve-se o desenvolvimento de um novo módulo de análise de dados que visa, usando métodos de identificação de genes diferencialmente expressos, identificar genes que se encontram diferencialmente expressos entre dois ou mais grupos experimentais. No final, é apresentado o trabalho resultante, a nível de interfaces gráficas e funcionamento.
Deoxyribonucleic acid (DNA) microarrays are an important technology for the analysis of gene expression. They allow measuring the expression of genes among several samples in order to, for example, identify genes whose expression varies with the administration of a certain drug. A microarray slide measures the expression level of thousands of genes in a sample at the same time, and an experiment can include various slides, leading to a lot of data to be processed and analyzed, with the aid of computerized means. This dissertation includes a review of methods and software tools used in the analysis of microarray experimental data. Then it is described the development of a new data analysis module that intends, using methods of identifying differentially expressed genes, to identify genes that are differentially expressed between two more groups. Finally, the resulting work is presented, describing its graphical interface and structural design.

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21

Zhou, Feng. "Contaminated Chi-square Modeling and Its Application in Microarray Data Analysis." UKnowledge, 2014. http://uknowledge.uky.edu/statistics_etds/7.

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Mixture modeling has numerous applications. One particular interest is microarray data analysis. My dissertation research is focused on the Contaminated Chi-Square (CCS) Modeling and its application in microarray. A moment-based method and two likelihood-based methods including Modified Likelihood Ratio Test (MLRT) and Expectation-Maximization (EM) Test are developed for testing the omnibus null hypothesis of no contamination of a central chi-square distribution by a non-central Chi-Square distribution. When the omnibus null hypothesis is rejected, we further developed the moment-based test and the EM test for testing an extra component to the Contaminated Chi-Square (CCS+EC) Model. The moment-based approach is easy and there is no need for re-sampling or random field theory to obtain critical values. When the statistical models are complicated such as large mixtures of dimensional distributions, MLRT and EM test may have better power than moment based approaches, and the MLRT and EM tests developed herein enjoy an elegant asymptotic theory.

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22

Szeto, Lap Keung. "Clustering analysis of microarray gene expression data /." access full-text access abstract and table of contents, 2005. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?mphil-it-b19885817a.pdf.

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Thesis (M.Phil.)--City University of Hong Kong, 2005.
"Submitted to Department of Computer Engineering and Information Technology in partial fulfillment of the requirements for the degree of Master of Philosophy" Includes bibliographical references (leaves 70-79)

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23

Lee, Kyeong Eun. "Bayesian models for DNA microarray data analysis." Diss., Texas A&M University, 2005. http://hdl.handle.net/1969.1/2465.

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Selection of signi?cant genes via expression patterns is important in a microarray problem. Owing to small sample size and large number of variables (genes), the selection process can be unstable. This research proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables in a regression setting and use a Bayesian mixture prior to perform the variable selection. Due to the binary nature of the data, the posterior distributions of the parameters are not in explicit form, and we need to use a combination of truncated sampling and Markov Chain Monte Carlo (MCMC) based computation techniques to simulate the posterior distributions. The Bayesian model is ?exible enough to identify the signi?cant genes as well as to perform future predictions. The method is applied to cancer classi?cation via cDNA microarrays. In particular, the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the method is used to identify the set of signi?cant genes to classify BRCA1 and others. Microarray data can also be applied to survival models. We address the issue of how to reduce the dimension in building model by selecting signi?cant genes as well as assessing the estimated survival curves. Additionally, we consider the wellknown Weibull regression and semiparametric proportional hazards (PH) models for survival analysis. With microarray data, we need to consider the case where the number of covariates p exceeds the number of samples n. Speci?cally, for a given vector of response values, which are times to event (death or censored times) and p gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the responsible genes, which are controlling the survival time. This approach enables us to estimate the survival curve when n << p. In our approach, rather than ?xing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional ?exibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in e?ect works as a penalty. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method. We demonstrate the use of the methodology with (a) di?use large B??cell lymphoma (DLBCL) complementary DNA (cDNA) data and (b) Breast Carcinoma data. Lastly, we propose a mixture of Dirichlet process models using discrete wavelet transform for a curve clustering. In order to characterize these time??course gene expresssions, we consider them as trajectory functions of time and gene??speci?c parameters and obtain their wavelet coe?cients by a discrete wavelet transform. We then build cluster curves using a mixture of Dirichlet process priors.

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Moertel, Luke Paul Frank, and mobileluke@hotmail com /. Luke Moertel@qimr edu au. "Microarray Analysis of the Schistosoma japonicum Transcriptome." Central Queensland University. Chemical and Biomedical Sciences, 2007. http://library-resources.cqu.edu.au./thesis/adt-QCQU/public/adt-QCQU20070705.120939.

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Schistosomiasis, a disease of humans caused by helminth parasites of the genus Schistosoma, kills 200 to 500 thousand people annually, endangering over 600 million people world-wide with 200 million people infected in 2003 [1, 2]. Three species of schistosome are primarily responsible for human infections, namely, Schistosoma haematobium, endemic to Africa, India, and the Middle East, S. mansoni, endemic to Africa / South America, and S. japonicum endemic to China and the Philippines [3]. The major pathological effects of schistosomiasis result from the deposition of parasite ova in human tissues and the subsequent intense granulomatous response induced by these eggs. There is a high priority to provide an effective sub-unit vaccine against these schistosome flukes, using proteins encoded by cDNAs expressed by the parasites at critical phases of their development. One technique that may expedite this gene identification is the use of microarrays for expression analysis. A 22,575 feature custom oligonucleotide DNA microarray designed from public domain databases of schistosome ESTs (Expressed Sequence Tags) was used to explore differential gene expression between the Philippine (SJP) and Chinese (SJC) strains of S. japonicum, and between males and females. It was found that 593, 664 and 426 probes were differentially expressed between the two geographical strains when mix sexed adults, male worms and female worms were compared respectively. Additionally, the study revealed that 1,163 male- and 1,016 female-associated probes were differentially expressed in SJP whereas 1,047 male- and 897 female-associated probes were differentially expressed in SJC [4]. Further to this, a detailed real time PCR expression study was used to explore the differential expression of eight genes of interest throughout the SJC life cycle, which showed that several of the genes were down-regulated in different life cycle stages. The study has greatly expanded previously published data of strain and gender-associated differential expression in S. japonicum. Further, the new data will provide a stepping stone for understanding the complexities of the biology, sexual differentiation, maturation, and development of human schistosomes, signaling new approaches for identifying novel intervention and diagnostic targets against schistosomiasis [4].

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25

Eijssen, Lars Maria Theo. "Analysis of microarray gene expression data sets." [Maastricht : Maastricht : Universiteit Maastricht] ; University Library, Universiteit Maastricht [host], 2006. http://arno.unimaas.nl/show.cgi?fid=6830.

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26

Guile, Geofrrey Robert. "Boosting ensemble techniques for Microarray data analysis." Thesis, University of East Anglia, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.518361.

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27

Rao, Youlan. "Statistical Analysis of Microarray Experiments in Pharmacogenomics." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1244756072.

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28

Kennedy, Richard Ellis. "Probe Level Analysis of Affymetrix Microarray Data." VCU Scholars Compass, 2008. http://hdl.handle.net/10156/1637.

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29

Gabbur, Prasad. "Machine Learning Methods for Microarray Data Analysis." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/195829.

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Microarrays emerged in the 1990s as a consequence of the efforts to speed up the process of drug discovery. They revolutionized molecular biological research by enabling monitoring of thousands of genes together. Typical microarray experiments measure the expression levels of a large numberof genes on very few tissue samples. The resulting sparsity of data presents major challenges to statistical methods used to perform any kind of analysis on this data. This research posits that phenotypic classification and prediction serve as good objective functions for both optimization and evaluation of microarray data analysis methods. This is because classification measures whatis needed for diagnostics and provides quantitative performance measures such as leave-one-out (LOO) or held-out prediction accuracy and confidence. Under the classification framework, various microarray data normalization procedures are evaluated using a class label hypothesis testing framework and also employing Support Vector Machines (SVM) and linear discriminant based classifiers. A novel normalization technique based on minimizing the squared correlation coefficients between expression levels of gene pairs is proposed and evaluated along with the other methods. Our results suggest that most normalization methods helped classification on the datasets considered except the rank method, most likely due to its quantization effects.Another contribution of this research is in developing machine learning methods for incorporating an independent source of information, in the form of gene annotations, to analyze microarray data. Recently, genes of many organisms have been annotated with terms from a limited vocabulary called Gene Ontologies (GO), describing the genes' roles in various biological processes, molecular functions and their locations within the cell. Novel probabilistic generative models are proposed for clustering genes using both their expression levels and GO tags. These models are similar in essence to the ones used for multimodal data, such as images and words, with learning and inference done in a Bayesian framework. The multimodal generative models are used for phenotypic class prediction. More specifically, the problems of phenotype prediction for static gene expression data and state prediction for time-course data are emphasized. Using GO tags for organisms whose genes have been studied more comprehensively leads to an improvement in prediction. Our methods also have the potential to provide a way to assess the quality of available GO tags for the genes of various model organisms.

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Model, Fabian. "Statistical analysis of microarray based DNA methylation data." [S.l.] : [s.n.], 2007. http://opus.kobv.de/tuberlin/volltexte/2007/1612.

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Ulgen, Burcin Emre. "Robust Estimation And Hypothesis Testing In Microarray Analysis." Phd thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612352/index.pdf.

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Microarray technology allows the simultaneous measurement of thousands of gene expressions simultaneously. As a result of this, many statistical methods emerged for identifying differentially expressed genes. Kerr et al. (2001) proposed analysis of variance (ANOVA) procedure for the analysis of gene expression data. Their estimators are based on the assumption of normality, however the parameter estimates and residuals from this analysis are notably heavier-tailed than normal as they commented. Since non-normality complicates the data analysis and results in inefficient estimators, it is very important to develop statistical procedures which are efficient and robust. For this reason, in this work, we use Modified Maximum Likelihood (MML) and Adaptive Maximum Likelihood estimation method (Tiku and Suresh, 1992) and show that MML and AMML estimators are more efficient and robust. In our study we compared MML and AMML method with widely used statistical analysis methods via simulations and real microarray data sets.

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George, Florence. "Johnson's system of distributions and microarray data analysis." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002040.

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Laurell, Cecilia. "Microarray Based Gene Expression Analysis in Cancer Research." Doctoral thesis, Stockholm : School of Biotechnology, Royal Institute of Technology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4244.

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34

Fraser, Karl. "cDNA microarray image analysis : a fully automated framework." Thesis, Brunel University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.429240.

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Lattimore, Brian Steven. "Novel methodologies for the analysis of microarray data." Thesis, University of Reading, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.483566.

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Green, Gerwyn. "Statistical methods for the analysis of microarray data." Thesis, Lancaster University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.533081.

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37

Lynch, O'Neil. "Mixture distributions with application to microarray data analysis." Scholar Commons, 2009. http://scholarcommons.usf.edu/etd/2075.

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The main goal in analyzing microarray data is to determine the genes that are differentially expressed across two types of tissue samples or samples obtained under two experimental conditions. In this dissertation we proposed two methods to determine differentially expressed genes. For the penalized normal mixture model (PMMM) to determine genes that are differentially expressed, we penalized both the variance and the mixing proportion parameters simultaneously. The variance parameter was penalized so that the log-likelihood will be bounded, while the mixing proportion parameter was penalized so that its estimates are not on the boundary of its parametric space. The null distribution of the likelihood ratio test statistic (LRTS) was simulated so that we could perform a hypothesis test for the number of components of the penalized normal mixture model. In addition to simulating the null distribution of the LRTS for the penalized normal mixture model, we showed that the maximum likelihood estimates were asymptotically normal, which is a first step that is necessary to prove the asymptotic null distribution of the LRTS. This result is a significant contribution to field of normal mixture model. The modified p-value approach for detecting differentially expressed genes was also discussed in this dissertation. The modified p-value approach was implemented so that a hypothesis test for the number of components can be conducted by using the modified likelihood ratio test. In the modified p-value approach we penalized the mixing proportion so that the estimates of the mixing proportion are not on the boundary of its parametric space. The null distribution of the (LRTS) was simulated so that the number of components of the uniform beta mixture model can be determined. Finally, for both modified methods, the penalized normal mixture model and the modified p-value approach were applied to simulated and real data.

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Carter, Ben. "Statistical methodology for the analysis of microarray data." Thesis, University of Reading, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436616.

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Price, Lynn. "Microarray analysis of TBX5 and GATA4 transcriptional targets." Thesis, University of Nottingham, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442255.

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Moss, Charlotte. "Microarray analysis of tamoxifen resistance in breast cancer." Thesis, Queen Mary, University of London, 2009. http://qmro.qmul.ac.uk/xmlui/handle/123456789/483.

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Tamoxifen remains a widely used hormone therapy for pre and postmenopausal women with hormone receptor positive breast cancer in both adjuvant and metastatic disease settings. Resistance to this well tolerated and cost-effective drug limits its use. Only an improved understanding of the mechanisms of tamoxifen resistance will provide the basis for overcoming this phenomenon. Expression profiles from tamoxifen-resistant and sensitive MCF7 derived breast cancer cell lines were prepared, using Affymetrix HG_U133A cDNA microarrays. The data generated was analysed to identify novel pathways and genes associated with tamoxifen resistance or sensitivity. Selected genes, whose expression correlates with response to tamoxifen, were validated using RT-PCR in cell lines and following this, in situ hybridisation and immunohistochemistry on cell lines. Functional analyses of these genes were carried out: genes that were down-regulated in tamoxifen resistant MCF7 cells (HRASLS3, CTSD, CAXII) were selectively knocked down using RNA interference. Cell lines stably over-expressing genes upregulated in the tamoxifen resistant MCF7s (ATP1B1, SOCS2, NR4A2) were selected. These manipulated cells were subsequently tested for their response to anti-oestrogen treatment. Another major marker in breast cancer is the ERBB2 proto-oncogene; overexpressed in 20% of breast carcinomas, it is associated with poor prognosis and hormone resistance. The transcriptional deregulation of ERBB2 in breast cancer may in part be mediated by the transcription factors AP-2 and . Previous studies have shown that ERBB2 expression is repressed by oestrogen activated oestrogen receptor and that AP-2 binding sites within the ERBB2 promoter and the intronic enhancer are required for this oestrogenic repression. Using RNA interference, AP-2 and were successfully knocked down in breast cancer cell lines MCF7, T47D and ZR75-1. These have been used to investigate the effect of AP-2 loss on ERBB2 expression in hormonally manipulated cells.

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Wang, Qi. "Integrative Data Analysis of Microarray and RNA-seq." Diss., North Dakota State University, 2018. https://hdl.handle.net/10365/29968.

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Background: Microarray and RNA sequencing (RNA-seq) are two commonly used high-throughput technologies for gene expression profiling for the past decades. For global gene expression studies, both techniques are expensive, and each has its unique advantages and limitations. Integrative analysis of these two types of data would provide increased statistical power, reduced cost, and complementary technical advantages. However, the complete different mechanisms of the high-throughput techniques make the two types of data highly incompatible. Methods: Based on the degrees of compatibility, the genes are grouped into different clusters using a novel clustering algorithm, called Boundary Shift Partition (BSP). For each cluster, a linear model is fitted to the data and the number of differentially expressed genes (DEGs) is calculated by running two-sample t-test on the residuals. The optimal number of cluster can be determined using the selection criteria that is penalized on the number of parameters for model fitting. The method was evaluated using the data simulated from various distributions and it was compared with the conventional K-means clustering method, Hartigan-Wong’s algorithm. The BSP algorithm was applied to the microarray and RNA-seq data obtained from the embryonic heart tissues from wild type mice and Tbx5 mice. The raw data went through multiple preprocessing steps including data transformation, quantile normalization, linear model, principal component analysis and probe alignments. The differentially expressed genes between wild type and Tbx5 are identified using the BSP algorithm. Results: The accuracies of the BSP algorithm for the simulation data are higher than those of Hartigan-Wong’s algorithm for the cases with smaller standard deviations across the five different underlying distributions. The BSP algorithm can find the correct number of the clusters using the selection criteria. The BSP method identifies 584 differentially expressed genes between the wild type and Tbx5 mice. A core gene network developed from the differentially expressed genes showed a set of key genes that were known to be important for heart development. Conclusion: The BSP algorithm is an efficient and robust classification method to integrate the data obtained from microarray and RNA-seq.

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Papana, Ariadni. "Tools for Comprehensive Statistical Analysis of Microarray Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1207243877.

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43

Kapur, Karen Anita. "Low-level analysis of microarray probes on exon-targeting microarrays : modeling background, gene expression and cross-hybridization /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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Raghavan, Vijay Anand. "Design and application of the Kentucky Microarray Analysis Suite." Thesis, Montana State University, 2006. http://etd.lib.montana.edu/etd/2006/raghavan/RaghavanV1206.pdf.

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In recent years, microarrays have become the most widely used standard in the study of gene expression. The biggest problem in microarray data analysis is the dimensionality of the data, compared to other more traditional biomedical research methods. The inherent nature of the data, and the problems associated with the microarray data analysis, has led to the development of many methods for microarray data analysis. Microarray data analysis methods are commonly classified into Class Discovery methods e.g. clustering, Class Comparison methods e.g. predicting differentially expressed genes, and Class Prediction methods e.g. classification. In this thesis, a new microarray analysis tool called Kentucky Microarray Analysis Suite that has all the three major microarray analysis methods is introduced. As a proof of concept Affymetrix array data related to aging in C. elegans is analyzed with the Kentucky Microarray Analysis Suite and the results are presented.

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Soneji, Sharnit. "Statistical Analysis of cDNA Microarray Directed by Gene Function." Thesis, Birkbeck (University of London), 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487759.

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Microarrays allow the expression level of thousands of genes to be measured simultaneously. This study will address analytical issues predominantly concerned with eDNA arrays. These include normalisation and data preprocessing, leading to an assessment of cluster analysis and the integration of database information to elucidate functional classes of biological relevance. This is then extended further to classify genes of unknown function using Markov Random Fields. I Modelling of uneven surface trends were considered in a new 2D-normalisation method which outperformed the popular loess method which concentrated on printing pin effects. With respect to cluster analysis, a new method to identify the number of clusters in higher dimension da~a is proposed which provides a visual way of determining at which point over-fitting of the data will occur. Once partitioned, functional information was incorporated to find enrichment of classes in clusters using a new application of X2 bootstrapping, which provides a very robust way of identifying these groups. A novel use of correspondence analysis was applied to the contingency tables produced from the cluster over class analysis which was used to show that functionally related groups were acting in concert when scrutinising the projection' of these classes onto three dimensions. The last part of this study attempted the use of Markov Random Fields to assign function to genes of unknown function using M. tuberculosis and E. coli data. The ability to determine function from the data used in this work was limited, but the method implemented in this study showed improvement over previous attempts.

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Scheid, Stefanie Christina. "Novel concepts for the significance analysis of microarray data." [S.l.] : [s.n.], 2006. http://www.diss.fu-berlin.de/2006/643/index.html.

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Potter, Nicola Emma. "Combined genomic and expression microarray analysis of paediatric astrocytoma." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/1444521/.

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Although astrocytomas account for 40% of brain tumours in children, little is known about the genetics of these paediatric tumours. Indeed, 85% of low-grade (WHO grade I and II) and 50% of high-grade (WHO grade III and IV) paediatric astrocytoma have a normal karyotype. The aim of this study was to identify non-random genetic aberrations in different grades of paediatric astrocytoma at both the genomic and expression levels. Affymetrix genechip technology and array comparative genomic hybridisation (aCGH) have been used to generate gene expression profiles of 35 paediatric astrocytoma short-term cell cultures of all grades and 19 pilocytic (PA, grade I) biopsies and identify copy number alterations (CNA) in 32 paediatric astrocytoma short-term cell cultures of all grades and 11 pilocytic biopsies. The PAI biopsy samples have a distinct expression profile compared to normal brain with 1844 genes being differentially expressed in all samples. The KEGG pathway most influenced by these genes is antigen processing and presentation, with the majority of genes being up- regulated. Addition pathways altered include PI3K signalling and MAPK signalling. Only single clone CNAs were detected in PAI including alterations at Ip36.32-p36.3, 14ql2 and 22q33.33 which were lost or gained in the majority of samples. The clone at 14ql2 is located in a region of large-scale copy number alteration (LCV). Alterations at this site have been linked to increased risk of paediatric solid tumour development. No known genes are located within the clone site. However, FOXG1B is adjacent to the clone region and is significantly down-regulated in PAI compared to normal brain. Hierarchical clustering of the short-term cultures according to expression profile similarity demonstrated that paediatric astrocytoma can be grouped into low and high-grade tumours by molecular signature. Furthermore, approximately half of paediatric glioblastoma multiforme (GBMIV, grade IV) clustered with 7 adult GBMIV cultures, suggesting that some paediatric GBMIV are genotypically similar to those arising in adults. KEGG pathways influenced by differential gene expression include Wnt signalling and the cell cycle pathway, with the finding that same pathways are being disrupted to varying extents in low and high-grade paediatric astrocytoma. The frequency of CNAs was similar to those previously reported, with gain of all or part of chromosome 7 as the most common alteration. Correlations between gene expression and CNAs were also identified in the short-term cultures.

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Evans, Daniel T. "A SNP Microarray Analysis Pipeline Using Machine Learning Techniques." Ohio University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1289950347.

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Lin, Wen. "Meta analysis methods for microarray data and proteomics data." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1692119641&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Wennmalm, Kristian. "Analytical strategies for identifying relevant phenotypes in microarray data /." Stockholm, 2007. http://diss.kib.ki.se/2007/978-91-7357-401-3/.

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