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Title: Statistical Analysis of a Time-course Nasopharyngeal Carcinoma Gene Expression Data.
Keywords: Nasopharyngeal Carcinoma, Hierarchical Clustering, Principal Component Analysis, Gene Expression Data, Time-course Regulation, Differential Expression
Issue Date: 18-Oct-2008
Citation: MD. ATIKUR RAHMAN KHAN (2008-10-18). Statistical Analysis of a Time-course Nasopharyngeal Carcinoma Gene Expression Data.. ScholarBank@NUS Repository.
Abstract: A common goal of microarray experiment is to identify genes that are differentially expressed under different biological conditions. In this thesis, we studied a time-course microarray gene expressions under the action of CYC202 on 3 cell lines. The intended aim is to investigate the time-course regulation and differential expression in relation to the responses. However, it is challenging to analyze this dataset. The difficulties arise mainly from at least two aspects: no repeated measurements, and a small number of time point measurements. We set out more modest objectives in our work: to explore the different aspects of this dataset, to possibly uncover the salient features of the dataset; and hopefully to shed light on how the genes respond to the action of CYC202.Statistical methods we have attempted include (i) good hierarchical clustering based on appropriate choice of distance measure to explore which sets of genes respond similarly or differently in relation to different cell lines; (ii) principal component analysis for dimension reduction to understand and visualize the dataset, and perhaps to discover which time point(s) are more informative; (iii) association study of coregulation and reverse-regulation using the time-course patterns and profile analysis.Though these methods do not succeed to give concordant results, in each statistical method some subsets of genes were identified to behave differently from the majority of the genes. Constrained by our knowledge in molecular biology, we cannot assess whether these genes are really of biological importance. This underlines the need for close collaboration with the biologists to unearth the biological information in this interesting and challenging dataset.
Appears in Collections:Master's Theses (Open)

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