English  |  正體中文  |  简体中文  |  Items with full text/Total items : 87924/117065 (75%)
Visitors : 23344964      Online Users : 172
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/113051
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/113051


    Title: Gene set analysis using sufficient dimension reduction
    Authors: 薛慧敏
    Hsueh, Huey-Miin
    Tsai, Chen-An
    Contributors: 統計系
    Keywords: Bins;Error analysis;Genes;Biological pathways;Co-expression;Microarray data sets;Non linear;Number of methods;Real data analysis;Simulation studies;Sufficient dimension reduction;Gene expression;protein p53;TP53 protein, human;African American;biology;computer simulation;DNA microarray;ethnology;gene expression profiling;gene regulatory network;genetics;genotype;human;male;phenotype;procedures;prostate tumor;African Americans;Computational Biology;Computer Simulation;Gene Expression Profiling;Gene Regulatory Networks;Genotype;Humans;Male;Oligonucleotide Array Sequence Analysis;Phenotype;Prostatic Neoplasms;Tumor Suppressor Protein p53
    Date: 2016-02
    Issue Date: 2017-09-15 16:09:00 (UTC+8)
    Abstract: Background: Gene set analysis (GSA) aims to evaluate the association between the expression of biological pathways, or a priori defined gene sets, and a particular phenotype. Numerous GSA methods have been proposed to assess the enrichment of sets of genes. However, most methods are developed with respect to a specific alternative scenario, such as a differential mean pattern or a differential coexpression. Moreover, a very limited number of methods can handle either binary, categorical, or continuous phenotypes. In this paper, we develop two novel GSA tests, called SDRs, based on the sufficient dimension reduction technique, which aims to capture sufficient information about the relationship between genes and the phenotype. The advantages of our proposed methods are that they allow for categorical and continuous phenotypes, and they are also able to identify a variety of enriched gene sets. Results: Through simulation studies, we compared the type I error and power of SDRs with existing GSA methods for binary, triple, and continuous phenotypes. We found that SDR methods adequately control the type I error rate at the pre-specified nominal level, and they have a satisfactory power to detect gene sets with differential coexpression and to test non-linear associations between gene sets and a continuous phenotype. In addition, the SDR methods were compared with seven widely-used GSA methods using two real microarray datasets for illustration. Conclusions: We concluded that the SDR methods outperform the others because of their flexibility with regard to handling different kinds of phenotypes and their power to detect a wide range of alternative scenarios. Our real data analysis highlights the differences between GSA methods for detecting enriched gene sets.
    Relation: BMC Bioinformatics, 17, 74
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1186/s12859-016-0928-6
    DOI: 10.1186/s12859-016-0928-6
    Appears in Collections:[統計學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    15.pdf1540KbAdobe PDF148View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback