A novel approach to DNA copy number data segmentation.
A novel approach to DNA copy number data segmentation.
复制标题
一种新的 DNA 拷贝数数据分割方法。
DOI：
10.1142/s0219720011005343
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发表时间：
2011
影响因子：
1
通讯作者：
Xiao,Guanghua
中科院分区：
文献类型：
--
作者：
Wang,Siling;Wang,Yuhang;Xie,Yang;Xiao,Guanghua
DNA拷贝数（DNA copy number，DCN）是基因组中某一区域的DNA拷贝数。DCN的改变与不同肿瘤的发生发展密切相关。近年来，微阵列技术被用于同时检测肿瘤样品中多个位点的DCN变化。由此产生的DCN数据通常非常嘈杂，并且肿瘤样本通常被正常细胞污染。基于阵列的DCN数据的计算分析的目标是从原始DCN数据推断底层DCN。以前的方法没有明确的模型的肿瘤/正常细胞的混合比例，他们不能输出段与DCN annotations.We开发了一种新的基于模型的方法，使用最小描述长度（MDL）的DCN数据分割的原则。我们的新方法可以输出每个染色体片段的潜在DCN，同时推断测试样本中潜在的肿瘤比例。实验结果表明，我们的方法实现了更好的准确率平均相比，以前的三种方法，即循环二进制分割，隐马尔可夫模型和超声波。
DNA copy number (DCN) is the number of copies of DNA at a region of a genome. The alterations of DCN are highly associated with the development of different tumors. Recently, microarray technologies are being employed to detect DCN changes at many loci at the same time in tumor samples. The resulting DCN data are often very noisy, and the tumor sample is often contaminated by normal cells. The goal of computational analysis of array-based DCN data is to infer the underlying DCNs from raw DCN data. Previous methods for this task do not model the tumor/normal cell mixture ratio explicitly and they cannot output segments with DCN annotations.We developed a novel model-based method using the minimum description length (MDL) principle for DCN data segmentation. Our new method can output underlying DCN for each chromosomal segment, and at the same time, infer the underlying tumor proportion in the test samples. Empirical results show that our method achieves better accuracies on average as compared to three previous methods, namely Circular Binary Segmentation, Hidden Markov Model and Ultrasome.