Detection of gene copy number changes in CGH microarrays using a spatially correlated mixture model
Detection of gene copy number changes in CGH microarrays using a spatially correlated mixture model
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DOI：
10.1093/bioinformatics/btl035
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发表时间：
2006-04-15
期刊：
影响因子：
5.8
通讯作者：
Richardson, S
中科院分区：
文献类型：
--
作者：
Broët, P;Richardson, S
动机：研究基因拷贝数变化的比较基因组杂交阵列实验给统计分析带来了新的挑战，并呼吁采用结合染色体序列之间空间相关性的方法。为此，我们提出了一种名为CGHMix的新方法。它基于空间结构的混合模型，三种状态对应于未修改、缺失或扩增的基因组序列。推理在贝叶斯框架中执行。结果：使用模拟数据对CGHMix进行了验证，并与传统的非结构混合模型和最近提出的数据挖掘方法进行了比较。我们展示了CGHMix在对拷贝数变化进行分类方面的良好性能。此外，该方法还提供了对错误发现率的良好估计。我们还给出了一个癌症相关数据集的分析。
Motivation: Comparative genomic hybridization array experiments that investigate gene copy number changes present new challenges for statistical analysis and call for methods that incorporate spatial dependence between sequences along the chromosome. For this purpose, we propose a novel method called CGHmix. It is based on a spatially structured mixture model with three states corresponding to genomic sequences that are either unmodified, deleted or amplified. Inference is performed in a Bayesian framework. From the output, posterior probabilities of belonging to each of the three states are estimated for each genomic sequence and used to classify them.Results: Using simulated data, CGHmix is validated and compared with both a conventional unstructured mixture model and with a recently proposed data mining method. We demonstrate the good performance of CGHmix for classifying copy number changes. In Addition, the method provides a good estimate of the false discovery rate. We also present the analysis of a cancer related dataset.