DETECTION OF COPY NUMBER VARIATION

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 阵列比较基因组杂交(ACGH)可以识别基因组中拷贝数的变化。在使用计算全息数据或由各种阵列技术产生的其他类似数据分析拷贝数变化(CNV)时，关键的计算挑战是检测拷贝数变化的片段边界，并推断每个片段的拷贝数状态。在这个子项目中，我们开发了一个基于条件随机场(CRF)框架的新的统计模型，该模型可以有效地将数据平滑、分割和拷贝数状态解码结合到一个统一的框架中。我们的方法(称为CRF-CNV)在定义有意义的特征函数方面提供了极大的灵活性。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)方法进行似然优化，并在CG框架内开发了高效的前向/后向算法。使用已知拷贝数的真实数据以及具有现实假设的模拟数据对该方法进行了评估，并与两个流行的公开可用的程序进行了比较。实验结果表明，在两个数据集上，CRF-CNV在副本数量分配方面都优于基于贝叶斯隐马尔可夫模型的方法。与非参数方法相比，CRF-CNV在保持对真实数据相同召回率的同时，获得了更高的准确率，在模拟数据上的性能是相当的。

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.