Differentially Aberrant Region Detection in Array CGH Data Based on Nearest Neighbor Classification Performance

スポンサーリンク

概要

• 論文の詳細を見る

• Array CGH is a useful technology for detecting copy number aberrations in genome-wide scale. We study the problem of detecting differentially aberrant genomic regions in two or more groups of CGH arrays and estimating the statistical significance of those regions. An important property of array CGH data is that there are spatial correlations among probes, and we need to take this fact into consideration when we develop a computational algorithm for array CGH data analysis. In this paper we first discuss three difficult issues underlying this problem, and then introduce nearest-neighbor multivariate test in order to alleviate these difficulties. Our proposed approach has three advantages. First, it can incorporate the spatial correlation among probes. Second, genomic regions with different sizes can be analyzed in a common ground. And finally, the computational cost can be considerably reduced with the use of a simple trick. We demonstrate the effectiveness of our approach through an application to previously published array CGH data set on 75 malignant lymphoma patients.

著者

• Ishikawa Yuta

  Nagoya Institute of Technology

• Takeuchi Ichiro

  Nagoya Institute of Technology

関連論文

• Differentially Aberrant Region Detection in Array CGH Data Based on Nearest Neighbor Classification Performance
• Differentially Aberrant Region Detection in Array CGH Data Based on Nearest Neighbor Classification Performance
• Least-Squares Conditional Density Estimation
• Conditional Density Estimation Based on Density Ratio Estimation
• Conditional Density Estimation Based on Density Ratio Estimation
• Density Difference Estimation
• A Density-ratio Framework for Statistical Data Processing
• A Density-ratio Framework for Statistical Data Processing

スポンサーリンク