A Segmentation/Clustering model for the analysis of array CGH data
A Segmentation/Clustering model for the analysis of array CGH data
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DOI：
10.1111/j.1541-0420.2006.00729.x
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发表时间：
2007-09-01
期刊：
影响因子：
1.9
通讯作者：
Daudin, J.-J.
中科院分区：
文献类型：
--
作者：
Picard, F.;Robin, S.;Daudin, J.-J.
微阵列- cgh（比较基因组杂交）实验用于检测和绘制染色体失衡。CGH谱可以看作是代表基因组中均匀区域的一系列片段，其代表序列平均具有相同的相对拷贝数。分割方法构成了分析的自然框架，但它们不提供检测片段的生物学状态。我们提出了一种新的分割/聚类模型，将分割模型与混合模型相结合。提出了一种新的混合算法——动态规划-期望最大化（DP-EM），通过极大似然估计模型的参数。该算法结合了DP算法和EM算法。我们还提出了一种模型选择启发式方法来选择簇的数量和段的数量。本文给出了一个基于公开数据集的例子。我们将我们的方法与分割方法和隐马尔可夫模型进行了比较，并表明新的分割/聚类模型是一种很有前途的替代方法，可以应用于更一般的信号处理环境。
Microarray-CGH (comparative genomic hybridization) experiments are used to detect and map chromosomal imbalances. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose representative sequences share the same relative copy number on average. Segmentation methods constitute a natural framework for the analysis, but they do not provide a biological status for the detected segments. We propose a new model for this segmentation/clustering problem, combining a segmentation model with a mixture model. We present a new hybrid algorithm called dynamic programming-expectation maximization (DP-EM) to estimate the parameters of the model by maximum likelihood. This algorithm combines DP and the EM algorithm. We also propose a model selection heuristic to select the number of clusters and the number of segments. An example of our procedure is presented, based on publicly available data sets. We compare our method to segmentation methods and to hidden Markov models, and we show that the new segmentation/clustering model is a promising alternative that can be applied in the more general context of signal processing.