标题： 多重假设检验以估计随机块模型中的社区数量 显示英文标题

标题： Multiple Hypothesis Testing To Estimate The Number Of Communities in Stochastic Block Models

摘要： 单细胞RNA测序(scRNA-seq)数据集的聚类可以为细胞的生物功能提供关键见解。因此，使用基于网络的社区检测方法(一种更好的聚类方法)对scRNA-seq数据集进行聚类越来越普遍。在将基于网络的社区检测方法应用于scRNA-seq数据集时，主要挑战是这些方法\emph{先验}需要真实的社区或块的数量来估计社区成员身份。尽管已有用于估计社区数量的方法，但它们不适用于噪声较大的scRNA-seq数据集。此外，我们需要一种合适的方法从scRNA-seq数据集中提取合适的网络。为了解决这些问题，我们提出了一种两步解决方案：i) 一种简单的基于似然的方法，用于从scRNA-seq数据集中提取随机块模型(SBMs)，ii) 一种新的顺序多重检验(SMT)方法，用于估计SBMs中的社区数量。我们研究了SMT的理论性质，并在中等稀疏条件下建立了其一致性。此外，我们将SMT的数值性能与几种现有方法进行了比较。我们还表明，我们的方法在基准scRNA-seq数据集上估计社区数量方面与现有方法相比表现良好。最后，我们使用我们的方法来估计人类视网膜双极细胞单细胞数据集的子群。 显示英文摘要

摘要： Clustering of single-cell RNA sequencing (scRNA-seq) datasets can give key insights into the biological functions of cells. Therefore, it is not surprising that network-based community detection methods (one of the better clustering methods) are increasingly being used for the clustering of scRNA-seq datasets. The main challenge in implementing network-based community detection methods for scRNA-seq datasets is that these methods \emph{apriori} require the true number of communities or blocks for estimating the community memberships. Although there are existing methods for estimating the number of communities, they are not suitable for noisy scRNA-seq datasets. Moreover, we require an appropriate method for extracting suitable networks from scRNA-seq datasets. For addressing these issues, we present a two-fold solution: i) a simple likelihood-based approach for extracting stochastic block models (SBMs) out of scRNA-seq datasets, ii) a new sequential multiple testing (SMT) method for estimating the number of communities in SBMs. We study the theoretical properties of SMT and establish its consistency under moderate sparsity conditions. In addition, we compare the numerical performance of the SMT with several existing methods. We also show that our approach performs competitively well against existing methods for estimating the number of communities on benchmark scRNA-seq datasets. Finally, we use our approach for estimating subgroups of a human retina bipolar single cell dataset.