 |
English
|
正體中文
|
简体中文
|
Post-Print筆數 : 27 |
Items with full text/Total items : 109952/140891 (78%)
Visitors : 46246768
Online Users : 1096
|
|
|
Loading...
|
Please use this identifier to cite or link to this item:
https://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 |
Size | Format | |
|---|
| 15.pdf | | 1540Kb | Adobe PDF2 | 350 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
著作權政策宣告 Copyright Announcement1.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。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.
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。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(
nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
