M3C: Monte Carlo reference-based consensus clustering
M3C: Monte Carlo reference-based consensus clustering
复制标题
DOI：
10.1038/s41598-020-58766-1
复制
发表时间：
2020-02-04
影响因子：
4.6
通讯作者：
Barnes, Michael
中科院分区：
文献类型：
--
作者：
John, Christopher R.;Watson, David;Barnes, Michael
全基因组数据用于使用聚类算法将患者分层为精准医疗的类别。这个领域的一个常见问题是簇数(K)的选择。Monti共识聚类算法是一种广泛使用的方法，它使用稳定性选择来估计K。然而，该方法对较高的K值有偏倚，并且产生大量的假阳性。作为解决方案，我们开发了基于该算法的蒙特卡罗参考共识聚类（M3C）。M3C模拟了K值范围内稳定性分数的零分布，从而能够与真实数据进行比较，以消除偏差，并对结构的存在进行统计检验。M3C纠正了共识聚类的固有偏见，正如来自癌症基因组图谱（TCGA）的模拟和真实表达数据所证明的那样。为了测试M3C，我们开发了一种模拟多元高斯聚类的新方法clusterlab。
Genome-wide data is used to stratify patients into classes for precision medicine using clustering algorithms. A common problem in this area is selection of the number of clusters (K). The Monti consensus clustering algorithm is a widely used method which uses stability selection to estimate K. However, the method has bias towards higher values of K and yields high numbers of false positives. As a solution, we developed Monte Carlo reference-based consensus clustering (M3C), which is based on this algorithm. M3C simulates null distributions of stability scores for a range of K values thus enabling a comparison with real data to remove bias and statistically test for the presence of structure. M3C corrects the inherent bias of consensus clustering as demonstrated on simulated and real expression data from The Cancer Genome Atlas (TCGA). For testing M3C, we developed clusterlab, a new method for simulating multivariate Gaussian clusters.