Choi Hyungwon, Qin Zhaohui S, Ghosh Debashis
Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA.
J Comput Biol. 2010 Feb;17(2):121-37. doi: 10.1089/cmb.2009.0019.
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.
拷贝数畸变是癌症中基因组不稳定的一种常见形式。根据分子生物学的中心法则，基因表达与细胞遗传学事件密切相关，并在疾病表型中作为拷贝数变化的介导因素。因此，开发合适的统计方法来联合分析拷贝数和基因表达数据具有重要意义。这项工作描述了一种针对双层混合模型（DLMM）的新型贝叶斯推断方法，该模型直接对拷贝数数据的随机性质进行建模，并识别由于异常拷贝数导致的异常表达基因。进行了模拟研究以说明DLMM在拷贝数畸变频率、混杂效应和基因表达数据信噪比的各种设置下的稳健性。对真实乳腺癌数据的分析表明，DLMM能够识别肿瘤中特别归因于拷贝数畸变的表达变化，并且基于所选基因构建的样本特异性指数与相关临床信息相关。
