EDClust: an EM-MM hybrid method for cell clustering in multiple-subject single-cell RNA sequencing.
EDClust: an EM-MM hybrid method for cell clustering in multiple-subject single-cell RNA sequencing.
复制标题
EDClust：一种 EM-MM 混合方法，用于多受试者单细胞 RNA 测序中的细胞聚类。
DOI：
10.1093/bioinformatics/btac168
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Wu,Hao
中科院分区：
文献类型：
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作者：
Wei,Xin;Li,Ziyi;Ji,Hongkai;Wu,Hao
单细胞RNA测序（scRNA-seq）通过在单细胞水平上测量转录组学特征，彻底改变了生物学研究。随着scRNA-seq在大规模研究中的应用越来越多，当scRNA-seq数据来自多个受试者时，出现了适当聚类细胞的问题。一个挑战是受试者特异性变异;多个受试者的系统异质性可能对聚类准确性产生重大影响。现有的方法寻求解决这种影响遭受几个limitations.ResultsWe开发了一种新的统计方法，EDClust，多学科scRNA-seq细胞聚类。EDClust通过狄利克雷多项分布的混合物对序列读段计数进行建模，并明确说明细胞类型异质性、受试者异质性和聚类不确定性。一个EM-MM混合算法推导出最大化的数据似然和聚类的细胞。我们进行了一系列的模拟研究，以评估所提出的方法，并证明了出色的性能EDClust。对四个具有各种组织类型和物种的真实的scRNA-seq数据集的全面基准测试表明，与现有方法相比，EDClust的准确性有了实质性的提高。可用性和实施R包可在https://github.com/weix21/EDClust.Supplementary信息免费获得补充数据可在Bioinformatics online获得。
MotivationSingle-cell RNA sequencing (scRNA-seq) has revolutionized biological research by enabling the measurement of transcriptomic profiles at the single-cell level. With the increasing application of scRNA-seq in larger-scale studies, the problem of appropriately clustering cells emerges when the scRNA-seq data are from multiple subjects. One challenge is the subject-specific variation; systematic heterogeneity from multiple subjects may have a significant impact on clustering accuracy. Existing methods seeking to address such effects suffer from several limitations.ResultsWe develop a novel statistical method, EDClust, for multi-subject scRNA-seq cell clustering. EDClust models the sequence read counts by a mixture of Dirichlet-multinomial distributions and explicitly accounts for cell-type heterogeneity, subject heterogeneity and clustering uncertainty. An EM-MM hybrid algorithm is derived for maximizing the data likelihood and clustering the cells. We perform a series of simulation studies to evaluate the proposed method and demonstrate the outstanding performance of EDClust. Comprehensive benchmarking on four real scRNA-seq datasets with various tissue types and species demonstrates the substantial accuracy improvement of EDClust compared to existing methods.Availability and implementationThe R package is freely available at https://github.com/weix21/EDClust.Supplementary informationSupplementary data are available atBioinformaticsonline.