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Statistical methods for CGH array an...

Stanford University.

Statistical methods for CGH array analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical methods for CGH array analysis.
作者:	Wang, Pei.
面頁冊數:	85 p.
附註:	Adviser: Robert Tibshirani.
附註:	Source: Dissertation Abstracts International, Volume: 65-09, Section: B, page: 4651.
Contained By:	Dissertation Abstracts International65-09B.
標題:	Statistics.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3145571
ISBN:	0496045334

Statistical methods for CGH array analysis.
Wang, Pei.

Statistical methods for CGH array analysis. - 85 p.

Adviser: Robert Tibshirani.

Thesis (Ph.D.)--Stanford University, 2004.

Cluster Along Chromosomes (CLAC) is a method for calling gains and losses in CGH arrays. The idea is to build hierarchical clustering-style trees along each chromosome arm (or chromosome), and then selects the "interesting" clusters by controlling the False Discovery Rate (FDR) at a certain level. In addition, it provides a consensus summary across a set of arrays, as well as an estimate of the corresponding FDR. The method is illustrated through applications on a lung cancer cDNA microarray CGH data set as well as a BAC array CGH data set of aneuploid cell strains.

ISBN: 0496045334Subjects--Topical Terms:

182057
Statistics.

Statistical methods for CGH array analysis.
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520 # $a Cluster Along Chromosomes (CLAC) is a method for calling gains and losses in CGH arrays. The idea is to build hierarchical clustering-style trees along each chromosome arm (or chromosome), and then selects the "interesting" clusters by controlling the False Discovery Rate (FDR) at a certain level. In addition, it provides a consensus summary across a set of arrays, as well as an estimate of the corresponding FDR. The method is illustrated through applications on a lung cancer cDNA microarray CGH data set as well as a BAC array CGH data set of aneuploid cell strains.
520 # $a Genomic DNA copy number alterations are key genetic events in the development and progression of human cancers. The technique of microarray comparative genomic hybridization (array CGH) enables one to screen genome-wide for all possible regions with DNA copy number alterations, such as chromosome gains and losses, or localized amplifications and deletions. In this thesis, the author proposes two new methods addressing two different questions for the data analysis of array CGH.
520 # $a The next part of the thesis described another method, Boosted PRIM and its application to searching for oncogenic pathways. Boosted PRIM (Patient Rule Induction Method) is a new algorithm developed to search for oncogenic pathways using array-CGH data. Besides this application, the algorithm itself is suitable for general classification problems. PRIM is a variation of Tree-Based methods, seeking box-shaped regions in the feature space to separate different classes. The idea of Boosted PRIM is to implement PRIM-styled weak learners in Adaboost, one of the most popular boosting algorithms. We illustrate the performance of the method through some simulation studies as well as an application on a lung cancer array-CGH data set.
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