| dc.contributor | 統計系 | zh_TW |
| dc.creator (作者) | 薛慧敏 | zh_TW |
| dc.creator (作者) | Hsueh, Huey-Miin | en_US |
| dc.creator (作者) | Tsai, Chen-An | en_US |
| dc.date (日期) | 2016-02 | |
| dc.date.accessioned | 15-九月-2017 16:09:00 (UTC+8) | - |
| dc.date.available | 15-九月-2017 16:09:00 (UTC+8) | - |
| dc.date.issued (上傳時間) | 15-九月-2017 16:09:00 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/113051 | - |
| dc.description.abstract (摘要) | 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. | en_US |
| dc.format.extent | 1577528 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | BMC Bioinformatics, 17, 74 | en_US |
| dc.subject (關鍵詞) | 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 | en_US |
| dc.title (題名) | Gene set analysis using sufficient dimension reduction | en_US |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1186/s12859-016-0928-6 | |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1186/s12859-016-0928-6 | |
