• Login
    View Item 
    •   Home
    • Colleges & Programs
    • Medical College of Georgia (MCG)
    • Department of Biostatistics and Epidemiology
    • Department of Biostatistics and Epidemiology: Faculty Research and Publications
    • View Item
    •   Home
    • Colleges & Programs
    • Medical College of Georgia (MCG)
    • Department of Biostatistics and Epidemiology
    • Department of Biostatistics and Epidemiology: Faculty Research and Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Scholarly CommonsCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsThis CollectionTitleAuthorsIssue DateSubmit DateSubjects

    My Account

    LoginRegister

    About

    AboutCreative CommonsAugusta University LibrariesUSG Copyright Policy

    Statistics

    Display statistics

    Ranking analysis of F-statistics for microarray data.

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    1471-2105-9-142.pdf
    Size:
    305.7Kb
    Format:
    PDF
    Download
    Thumbnail
    Name:
    1471-2105-9-142-S1.pdf
    Size:
    79.37Kb
    Format:
    PDF
    Download
    Authors
    Tan, Yuan-De
    Fornage, Myriam
    Xu, Hongyan
    Issue Date
    2008-04-15
    URI
    http://hdl.handle.net/10675.2/91
    
    Metadata
    Show full item record
    Abstract
    BACKGROUND: Microarray technology provides an efficient means for globally exploring physiological processes governed by the coordinated expression of multiple genes. However, identification of genes differentially expressed in microarray experiments is challenging because of their potentially high type I error rate. Methods for large-scale statistical analyses have been developed but most of them are applicable to two-sample or two-condition data. RESULTS: We developed a large-scale multiple-group F-test based method, named ranking analysis of F-statistics (RAF), which is an extension of ranking analysis of microarray data (RAM) for two-sample t-test. In this method, we proposed a novel random splitting approach to generate the null distribution instead of using permutation, which may not be appropriate for microarray data. We also implemented a two-simulation strategy to estimate the false discovery rate. Simulation results suggested that it has higher efficiency in finding differentially expressed genes among multiple classes at a lower false discovery rate than some commonly used methods. By applying our method to the experimental data, we found 107 genes having significantly differential expressions among 4 treatments at <0.7% FDR, of which 31 belong to the expressed sequence tags (ESTs), 76 are unique genes who have known functions in the brain or central nervous system and belong to six major functional groups. CONCLUSION: Our method is suitable to identify differentially expressed genes among multiple groups, in particular, when sample size is small.
    Citation
    BMC Bioinformatics. 2008 Mar 6; 9:142
    ae974a485f413a2113503eed53cd6c53
    10.1186/1471-2105-9-142
    Scopus Count
    Collections
    Department of Biostatistics and Epidemiology: Faculty Research and Publications

    entitlement

    Related articles

    • Construction of null statistics in permutation-based multiple testing for multi-factorial microarray experiments.
    • Authors: Gao X
    • Issue date: 2006 Jun 15
    • Ranking analysis of microarray data: a powerful method for identifying differentially expressed genes.
    • Authors: Tan YD, Fornage M, Fu YX
    • Issue date: 2006 Dec
    • Tail posterior probability for inference in pairwise and multiclass gene expression data.
    • Authors: Bochkina N, Richardson S
    • Issue date: 2007 Dec
    • A unified framework for finding differentially expressed genes from microarray experiments.
    • Authors: Shaik JS, Yeasin M
    • Issue date: 2007 Sep 18
    • Using weighted permutation scores to detect differential gene expression with microarray data.
    • Authors: Guo X, Pan W
    • Issue date: 2005 Aug
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.