Tumor subclones detection with Dirichlet Process Mixture Model
| Autor: | Wu, Tsung-Yu, 吳宗祐 |
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| Rok vydání: | 2015 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 103 Cancer is a malignant cell tumor and a major cause of death throughout the world. In recent years, technology about biomarker detection has been greatly improved. We can apply biomarkers to detect the disease status. Moreover, the new tool called next-generation sequencing (NGS) can rapidly and accurately sequence billions of bases. The target of this study is to cluster the somatically mutated single nucleotide variants (SNV) detected by NGS into subclones according to the proportion of SNV. We hope that grouping subclones can help us to probe the cancer evolution. However, the copy number aberrations (CNA) will affect the information of data and make errors when calculating the proportion of SNV. Hence, we not only consider the SNV mutation but also the CNA mutation. Our model is a two-step model. Our first step is to use Allele-Specific Copy number Analysis of Tumors (ASCAT) to detect the CNA mutation, and our second step is to use Dirichlet Process Mixture Model (DPMM) with Beta-Binomial to detect the SNV mutation. The DPMM is a famous cluster method, but there is an issue of how to choose a suitable value for α. We will provide learning models for α that can achieve a more robust result. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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