A Double-Layered Mixture Model for the Joint Analysis of DNA Copy Number and Gene Expression Data
A Double-Layered Mixture Model for the Joint Analysis of DNA Copy Number and Gene Expression Data
复制标题
DOI：
10.1089/cmb.2009.0019
复制
发表时间：
2010-02-01
影响因子：
1.7
通讯作者：
Ghosh, Debashis
中科院分区：
文献类型：
--
作者：
Choi, Hyungwon;Qin, Zhaohui S.;Ghosh, Debashis
拷贝数异常是癌症基因组不稳定的一种常见形式。通过分子生物学的中心教条，基因表达与细胞遗传学事件密切相关，并作为疾病表型拷贝数变化的中介。因此，开发适当的统计方法来联合分析拷贝数和基因表达数据是很有意义的。描述了一种针对双层混合模型(DLMM)的新的贝叶斯推理方法，该方法直接对拷贝数数据的随机性质进行建模，并识别由于拷贝数异常而异常表达的基因。通过仿真研究，验证了DLMM在基因表达数据的拷贝数、变异频率、混杂效应和信噪比等不同设置下的稳健性。对真实乳腺癌数据的分析表明，DLMM能够识别特定可归因于肿瘤拷贝数异常的表达变化，并且基于所选基因建立的样本特异性指数与相关临床信息相关。
Copy number aberration is a common form of genomic instability in cancer. Gene expression is closely tied to cytogenetic events by the central dogma of molecular biology, and serves as a mediator of copy number changes in disease phenotypes. Accordingly, it is of interest to develop proper statistical methods for jointly analyzing copy number and gene expression data. This work describes a novel Bayesian inferential approach for a double-layered mixture model (DLMM) which directly models the stochastic nature of copy number data and identifies abnormally expressed genes due to aberrant copy number. Simulation studies were conducted to illustrate the robustness of DLMM under various settings of copy number aberration frequency, confounding effects, and signal-to-noise ratio in gene expression data. Analysis of a real breast cancer data shows that DLMM is able to identify expression changes specifically attributable to copy number aberration in tumors and that a sample-specific index built based on the selected genes is correlated with relevant clinical information.