一种快速灵活的aCGH数据分割方法
A Fast and Flexible Method for the Segmentation of aCGH Data
摘要：
阵列比较基因组杂交（aCGH）用于扫描整个基因组的DNA拷贝数的变化。aCGH数据分析中的中心任务是分割成共享相同DNA拷贝数的探针组。一些公知的分割方法遭受非常长的运行时间，阻止交互式数据分析。结果：我们提出了一种新的分割方法，基于小波分解和阈值，检测数据中的重要断点。我们的算法比领先的方法快1,000倍以上，性能相似。所提出的方法的另一个关键优点是其简单性和灵活性。由于其直观的结构，它可以很容易地概括为包含几种类型的边信息。在这里，我们考虑两个扩展，其中包括侧信息，指示每个测量的可靠性，并补偿测量噪声的变化。所得到的算法优于现有的方法，无论是在速度和性能方面，当应用于真正的高密度计算全息数据。可用性：可在http://www.ee.technion.ac.il/Sites/People/YoninaEldar/的软件选项卡下进行实施，联系方式：yonina@ee.technion.ac.il
英文摘要：
Motivation: Array Comparative Genomic Hybridization (aCGH) is used to scan the entire genome for variations in DNA copy number. A central task in the analysis of aCGH data is the segmentation into groups of probes sharing the same DNA copy number. Some well known segmentation methods suffer from very long running times, preventing interactive data analysis. Results: We suggest a new segmentation method based on wavelet decomposition and thresholding, which detects significant breakpoints in the data. Our algorithm is over 1,000 times faster than leading approaches, with similar performance. Another key advantage of the proposed method is its simplicity and flexibility. Due to its intuitive structure it can be easily generalized to incorporate several types of side information. Here we consider two extensions which include side information indicating the reliability of each measurement, and compensating for a changing variability in the measurement noise. The resulting algorithm outperforms existing methods, both in terms of speed and performance, when applied on real high density CGH data. Availability: Implementation is available under software tab at: http://www.ee.technion.ac.il/Sites/People/YoninaEldar/ Contact: yonina@ee.technion.ac.il
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
