论文标题
层次群集的选择性推断
Selective Inference for Hierarchical Clustering
论文作者
论文摘要
平均值差异的经典测试可以控制I型错误率，而当组定义了先验时。但是，当相反通过聚类定义这些组时，应用经典测试会产生极度膨胀的I型错误率。值得注意的是，即使使用两个独立的数据集来定义组并测试其平均值的差异，这个问题仍然存在。为了解决这个问题，在本文中，我们提出了一种选择性推理方法，以测试两个簇之间的均值差异。我们的过程通过计算基于数据的无效假设的选择来控制选择性I型错误率。我们描述了如何有效地计算使用与许多常用链接的团聚性层次聚类获得的群集的精确p值。我们将我们的方法应用于模拟数据和单细胞RNA-sequer-severing数据。
Classical tests for a difference in means control the type I error rate when the groups are defined a priori. However, when the groups are instead defined via clustering, then applying a classical test yields an extremely inflated type I error rate. Notably, this problem persists even if two separate and independent data sets are used to define the groups and to test for a difference in their means. To address this problem, in this paper, we propose a selective inference approach to test for a difference in means between two clusters. Our procedure controls the selective type I error rate by accounting for the fact that the choice of null hypothesis was made based on the data. We describe how to efficiently compute exact p-values for clusters obtained using agglomerative hierarchical clustering with many commonly-used linkages. We apply our method to simulated data and to single-cell RNA-sequencing data.
