DETECTION OF COPY NUMBER VARIATION

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 阵列比较基因组杂交（aCGH）允许鉴定跨基因组的拷贝数改变。使用aCGH数据或由各种阵列技术生成的其他类似数据分析拷贝数变异（CNV）的关键计算挑战是检测拷贝数变化的片段边界和推断每个片段的拷贝数状态。在这个子项目中，我们开发了一种新的统计模型的基础上的框架条件随机场（CRF），可以有效地结合联合收割机数据平滑，分割和拷贝数状态解码到一个统一的框架。我们的方法（称为CRF-CNV）提供了很大的灵活性，在定义有意义的功能。因此，它可以有效地将任意大小的局部空间信息集成到模型中。对于模型参数估计，我们采用了共轭梯度（CG）的似然优化方法，并在CG框架内开发了高效的前向/后向算法。该方法使用真实的数据与已知的拷贝数以及模拟数据与现实的假设进行评估，并与两个流行的公开可用的程序进行比较。实验结果表明，CRF-CNV优于基于贝叶斯隐马尔可夫模型的方法在两个数据集的拷贝数分配。与非参数方法相比，CRF-CNV在真实的数据上实现了更高的精确度，同时保持了相同的召回水平，并且它们在模拟数据上的性能相当。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Array comparative genomic hybridization (aCGH) allows identification of copy number alterations across genomes. The key computational challenge in analyzing copy number variations (CNVs) using aCGH data or other similar data generated by a variety of array technologies is the detection of segment boundaries of copy number changes and inference of the copy number state for each segment. In this subproject, we have developed a novel statistical model based on the framework of conditional random fields (CRFs) that can effectively combine data smoothing, segmentation and copy number state decoding into one unified framework. Our approach (termed CRF-CNV) provides great flexibilities in defining meaningful feature functions. Therefore, it can effectively integrate local spatial information of arbitrary sizes into the model. For model parameter estimations, we have adopted the conjugate gradient (CG) method for likelihood optimization and developed efficient forward/backward algorithms within the CG framework. The method is evaluated using real data with known copy numbers as well as simulated data with realistic assumptions, and compared with two popular publicly available programs. Experimental results have demonstrated that CRF-CNV outperforms a Bayesian Hidden Markov Model-based approach on both datasets in terms of copy number assignments. Comparing to a non-parametric approach, CRF-CNV has achieved much greater precision while maintaining the same level of recall on the real data, and their performance on the simulated data is comparable.