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Statistical methods for analysis of ...

Hong, Fangxin.

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Statistical methods for analysis of microarray time course gene expression data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical methods for analysis of microarray time course gene expression data./
作者:	Hong, Fangxin.
面頁冊數:	99 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-09, Section: B, page: 4647.
Contained By:	Dissertation Abstracts International65-09B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3148459
ISBN:	0496074733

Statistical methods for analysis of microarray time course gene expression data.
Hong, Fangxin.

Statistical methods for analysis of microarray time course gene expression data. - 99 p.

Source: Dissertation Abstracts International, Volume: 65-09, Section: B, page: 4647.

Thesis (Ph.D.)--University of California, Davis, 2004.

Since many biological systems and processes in health and diseases are dynamic systems, genome-wide time-course gene expression studies can often provide more insights into such systems. Time course studies with microarray technologies provide great potential for exploring the underlying mechanisms and dynamics of a given biological process. These gene expression data treasured over time are often called the microarray time course (MTC) gene expression data. Such studies are essential in biomedical research to understand biological phenomena that evolve in a temporal fashion.

ISBN: 0496074733Subjects--Topical Terms:

517247
Statistics.

Statistical methods for analysis of microarray time course gene expression data.
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500 $a Source: Dissertation Abstracts International, Volume: 65-09, Section: B, page: 4647.
500 $a Adviser: Hongzhe Lee.
502 $a Thesis (Ph.D.)--University of California, Davis, 2004.
520 $a Since many biological systems and processes in health and diseases are dynamic systems, genome-wide time-course gene expression studies can often provide more insights into such systems. Time course studies with microarray technologies provide great potential for exploring the underlying mechanisms and dynamics of a given biological process. These gene expression data treasured over time are often called the microarray time course (MTC) gene expression data. Such studies are essential in biomedical research to understand biological phenomena that evolve in a temporal fashion.
520 $a The unique features of microarray time course (MTC) data make most traditional statistical methods inapplicable. Rigorous statistical methods for analyzing such data are required to draw valid and informative statistical and biological conclusions. Under the framework of microarray time-course gene expression studies, we have focused on the following two problems. (1) One-sample problem---Identifying genes with temporal change in expression, which are defined as temporally regulated (TR) genes. (2) Two-sample problem---Identifying genes with different expression profiles between two experimental conditions (e.g. cancerous vs normal cell line), which are defined as temporally differentially expressed (TDE) genes.
520 $a For both problems, we formulated the biological questions into statistical multi-testing problems, and proposed an empirical Bayes method based on a hierarchical model to identify genes. We treated the data sampled at sparse time points as multidimensional vectors, and modelled them directly using a hierarchical model. For relatively densely sampled data, we used basis expansion (B-spline) functions to model the true gene expression trajectories and characterized the coefficients of basis functions by a hierarchical model. A Monte Carlo EM algorithm is developed for estimating both the gene-specific parameters and the hyper-parameters. We used the posterior probability based false discovery rate (FDR) criterion to identify the TR/TDE genes in order to control for the overall FDR. We illustrated the methods by using both simulated data sets and a data set from a microarray gene expression time course study of C. elegans developmental processes.
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