| dc.contributor.advisor | 郭訓志<br>蔡紋琦 | zh_TW |
| dc.contributor.advisor | Hsun-Chih Kuo<br>Wen-Chi Tsai | en_US |
| dc.contributor.author (作者) | 黃慧珍 | zh_TW |
| dc.contributor.author (作者) | Hui-Chen Huang | en_US |
| dc.creator (作者) | 黃慧珍 | zh_TW |
| dc.creator (作者) | Hui-Chen Huang | en_US |
| dc.date (日期) | 2005 | en_US |
| dc.date.accessioned | 2009-09-14 | - |
| dc.date.available | 2009-09-14 | - |
| dc.date.issued (上傳時間) | 2009-09-14 | - |
| dc.identifier (其他 識別碼) | G0093354011 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/30899 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計研究所 | zh_TW |
| dc.description (描述) | 93354011 | zh_TW |
| dc.description (描述) | 94 | zh_TW |
| dc.description.abstract (摘要) | 研究指出，在不同人類個體的DNA序列中，只有0.1%的基因組排列是相異的，而其餘的序列則是相同的。這些相異的基因組排列則被稱為單一核苷酸(SNP)。Affymetrix公司發展出一種DNA晶片技術稱之為Affymetrix GeneChip Mapping 100K SNP set，此晶片可用來決定單一核苷酸資料的基因類型(genotype)。Affymetrix公司採用預設「動態模型演算法」(DM)來決定基因型態。本論文的研究目的是探討與示範對於DM方法中預設的S值的四種修正方式。而這四種修正的方法分別是： (1) Standardized L value，(2) Median-polished L value，(3) Median-center L value，和(4) Median-standardized L value。為了比較S值與四種改進方法，本研究藉由SNP的模擬資料來進行比較。資料的模擬是基於利用改寫過的階層式之Bolstad模型（2004），而模擬模型的參數估計是利用華人細胞株及基因資料庫中95位台灣人的100K SNP資料。根據AA模型與AB模型模擬資料的基因型態正確率，Standardized L value是最好的判斷基因型態之方法。在另一方面，因為DM方法並不是設計來決定Null模型的基因型態，因此對於Null模型模擬資料的基因型態判斷會有問題。關於Null模型的基因型態判斷，本論文提供了一些簡短的討論與建議。然而，依然需要進一步的研究探討。 | zh_TW |
| dc.description.abstract (摘要) | It is known there is only 0.1% in the DNA sequences that is different among human beings, and the rest of them are the same. These differences in DNA sequences are defined as SNPs (Single Nucleotide Polymorphism). The Affymetrix, Inc. had developed a DNA chip technology called Affymetrix GeneChip Mapping 100K SNP set for SNP data used to determine the genotype call. The default algorithm applied by Affymetrix, Inc. to decide genotype calls is the Dynamic Model-based (DM) algorithm. This study aimed to investigate and demonstrate four different ways to modify the basic component used in DM algorithm, namely, the S value. These four modified methods include: (1) Standardized L value, (2) Median-polished L value, (3) Median-centered L value, and (4) Median-standardized L value. In order to compare the S value with the four modified L values, a simulation study was conducted. A hierarchical version of Bolstad’s model (2004) was adopted to simulate the SNP Data. The parameters for the simulation model were estimated based on 95 Taiwanese 100K SNPs data from Taiwan Han Chinese Cell and Genome bank. The Standardized L value was proven to be the best method based on the accuracy of the genotype calls determined according to the simulated data of AA model and AB model. On the other hand, the genotype call for simulated data under Null model is problematic since the DM approach is not designed to determine the Null model. We have given some brief discussion and remarks of the genotype call for Null model. However, further research is still needed.
| en_US |
| dc.description.tableofcontents | CHAPTER 1. INTRODUCTION 1 CHAPTER 2. DESCRIPTION OF DATA 3 2.1 Affymetrix GeneChip Mapping 100K SNP Set 3 2.2 The Real Data 6 2.2.1 Summary of PM and MM 6 2.2.2 The Summary of γ, the Difference between log2PM and log2MM 9 2.2.3 Estimate of Errors 10 CHAPTER 3. LITERATIRE REVIEW 12 3.1 Model-Based Expression Index (MBEI) 12 3.2 Microarray Suite 5.0 (MAS 5.0) 13 3.3 Robust Multi-Array Average (RMA) 14 3.4 Dynamic Model-Based Algorithm (DM) 15 3.4.1 Calculate Log Likelihood Values (L Value) 19 3.4.2 Calculate the Minimum Log Likelihood Ratio (S Value) 20 3.4.3 Genotype Calls by Using One-Sided Wilcoxon Signed Rank Test 21 CHAPTER 4. METHODOLOGY 22 4.1 Bolstad’s Model 22 4.2 Simulation Model 23 4.3 Four Modified L Values for DM 24 4.3.1 Standardized L Value 25 4.3.2 Median-Polished L Value 26 4.3.3 Median-Centered L Value 27 4.3.4 Median-Standardized L Value 27 CHAPTER 5. SIMULATION STUDY 29 5.1 Data Simulation 29 5.1.1 Settings for log2MM and Error Terms 30 5.1.2 Settings for γ= log2PM - log2MM 30 5.1.3 Settings for Number of Pixels in the Cell 32 5.1.4 Descriptive Statistics of Simulated Data 32 5.2 Determination of Genotype Calls for Simulated Data 33 5.3 Analysis of Simulated Data under AA Model 34 5.3.1 Wilcoxon Signed Rank Test for AA Model 34 5.3.2 T Test for AA Model 36 5.4 Analysis of Simulated Data under AB Model 37 5.4.1 Wilcoxon Signed Rank Test for AB Model 37 5.4.2 T Test for AB Model 39 5.5 Analysis of Simulated Data under Null Model 40 5.5.1 Wilcoxon Signed Rank Test for Null Model 40 5.5.2 T Test for Null Model 41 5.6 Remark for Determination of No Call 42 5.7 Intersection Union Test for Powers under AA Model and AB Model 44 CHAPTER 6. CONCLUSION AND FUTURE WORK 47 6.1 Conclusion 47 6.2 Future Work 47 REFERENCES 49 APPENDIX 51 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0093354011 | en_US |
| dc.subject (關鍵詞) | 動態模型演算法(DM) | zh_TW |
| dc.subject (關鍵詞) | 單一核苷酸差異(SNP) | zh_TW |
| dc.subject (關鍵詞) | Dynamic Model-based algorithm (DM) | en_US |
| dc.subject (關鍵詞) | Single Nucleotide Polymorphism (SNP) | en_US |
| dc.title (題名) | 動態模型演算法在100K SNP資料之模擬研究 | zh_TW |
| dc.title (題名) | Dynamic Model Based Algorithm on 100K SNP Data:A Simulation Study | en_US |
| dc.type (資料類型) | thesis | en |
| dc.relation.reference (參考文獻) | Affymetrix (2002). Statistical Algorithms Description Document. Technical report, Affymetrix. | zh_TW |
| dc.relation.reference (參考文獻) | Affymetrix. (2004). GeneChip DNA Analysis software GDAS User’s Guide. Version 3.0, Affymetrix. | zh_TW |
| dc.relation.reference (參考文獻) | Bolstad, B.M. (2004). Low-level Analysis of High-density Oligonucleotide Array Data: Background, Normalization and Summarization. Ph.D, dissertation, University of California, Berkely, USA. | zh_TW |
| dc.relation.reference (參考文獻) | Casella, G., Berger, R. L., (2002). Statistical Inference. DUXBURY. | zh_TW |
| dc.relation.reference (參考文獻) | Cutler, D. J., Zwick, M.E., Carrasquillo, M.M., Yohn, C.T., Tobin, K.P., Kashuk, C., Mathews D.J., Shah N.A., Eichler E.E., Warrington J.A., and Chakravarti A. (2001). High-throughput variation detection and genotyping using microarrays. Genome Res., 11, 1913–1925. | zh_TW |
| dc.relation.reference (參考文獻) | Di, X., Matsuzaki, H., Webster, T. A., Hubbell, E., Liu, G., Dong, S., Bartell D., Huang J., Chiles R., Yang G., Shen M., Kulp D., Kennedy G. C., Mei R., Jones K. W. and Cawley S. (2005). Dynamic model based algorithms for screening and genotyping over 100K SNPs on oligonucleotide microarrays. Bioinformatics, Vol. 21: 1958–1963. | zh_TW |
| dc.relation.reference (參考文獻) | Irizarry, R., Hobbs, B., Collin, F., Beazer-Barclay, Y. D., Antonellis, K. J., Scherf, U., and Speed, T. P. (2003). Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data. Biostatistics. 4, 249-264. | zh_TW |
| dc.relation.reference (參考文獻) | Li, C., and Wong, H. (2001). Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application. Genome Biology 2(8): research 0032.1–0032.11. | zh_TW |
| dc.relation.reference (參考文獻) | Li, C., and Wong, H. (2001). Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. Proceedings of the National Academy of Science USA, 98, 31-36. | zh_TW |
| dc.relation.reference (參考文獻) | Liu, W. M., Di, X., Yang, G., Matsuzaki, H., Huang, J., Mei, R., Ryder, T. B., Webster, T. A., Dong, S., Liu, G., Jones, K. W., Kennedy, G. C. and Kulp, D. (2003). . Algorithms for large-scale genotyping microarray. Bioinformatics, vol.19(18):2397-2403 | zh_TW |
