An all-statistics, high-speed algorithm for the analysis of copy number variation in genomes.
An all-statistics, high-speed algorithm for the analysis of copy number variation in genomes.
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DOI：
10.1093/nar/gkr137
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发表时间：
2011-07
影响因子：
14.9
通讯作者：
Lee HC
中科院分区：
文献类型：
--
作者：
Chen CH;Lee HC;Ling Q;Chen HR;Ko YA;Tsou TS;Wang SC;Wu LC;Lee HC
近年来，DNA拷贝数变异(CNV)的检测已成为了解癌症发病机制的重要手段。虽然现有的从微阵列数据中提取CNV的算法工作得相当好，但越来越大的样本量和更高分辨率的微阵列的趋势极大地增加了他们面临的挑战。在这里，我们提出了DNA分割分析(SAD)，这是一种用一种策略构建的聚类算法，其中所有操作决策都基于统计原理、测量理论和精确数学关系的简单和严格应用。与现有的软件包相比，SAD在公式上更简单，对用户更友好，速度更快，对内存的需求更小，提供更高的准确性，并为其预测提供定量统计数据。在这类算法中独一无二的是，SAD的运行时间与阵列大小成线性关系；在一个典型的现代笔记本上，它完成了∼1 S中25万个探针阵列和∼8 S中180万个探针阵列的高质量CNV分析。
Detection of copy number variation (CNV) in DNA has recently become an important method for understanding the pathogenesis of cancer. While existing algorithms for extracting CNV from microarray data have worked reasonably well, the trend towards ever larger sample sizes and higher resolution microarrays has vastly increased the challenges they face. Here, we present Segmentation analysis of DNA (SAD), a clustering algorithm constructed with a strategy in which all operational decisions are based on simple and rigorous applications of statistical principles, measurement theory and precise mathematical relations. Compared with existing packages, SAD is simpler in formulation, more user friendly, much faster and less thirsty for memory, offers higher accuracy and supplies quantitative statistics for its predictions. Unique among such algorithms, SAD's running time scales linearly with array size; on a typical modern notebook, it completes high-quality CNV analyses for a 250 thousand-probe array in ∼1 s and a 1.8 million-probe array in ∼8 s.
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