A method for calling gains and losses in array CGH data
A method for calling gains and losses in array CGH data
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DOI：
10.1093/biostatistics/kxh017
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发表时间：
2005-01-01
期刊：
影响因子：
2.1
通讯作者：
Tibshirani, R
中科院分区：
文献类型：
--
作者：
Wang, P;Kim, Y;Tibshirani, R
阵列CGH是一种强有力的癌症基因组研究技术，它使人们能够进行基因组范围内的遗传变异区域，如染色体的获得和丢失。或局部扩增和缺失。本文提出了一种新的分析阵列计算全息图数据的算法--“沿沿着聚类”（CLAC）。CLAC沿着每个染色体臂（或染色体）沿着构建分层聚类风格的树，然后通过将错误发现率（FDR）控制在一定水平来选择“感兴趣的”聚类。此外，它还提供了一组数组的共识摘要，以及相应FDR的估计值。我们说明的方法使用的肺癌微阵列CGH数据集以及BAC阵列CGH数据集的非整倍体细胞株的应用程序的CLAC。
Array CGH is a powerful technique for genomic studies of cancer, It enables one to carry out genome-wide screening for regions of genetic alterations, such as chromosome gains and losses. or localized amplifications and deletions. In this paper, we propose a new algorithm 'Cluster along chromosomes' (CLAC) for the analysis of array CGH data. CLAC builds hierarchical clustering-style trees along each chromosome arm (or chromosome), and then selects the 'interesting' clusters by controlling the False Discovery Rate (FDR) at a certain level. In addition, it provides a consensus summary across a set of arrays, as well as an estimate of the corresponding FDR. We illustrate the method using an application of CLAC on a lung cancer microarray CGH data set as well as a BAC array CGH data set of aneuploid cell strains.