.png){kind=icon}
返回
A parameter-free deep embedded clustering method for single-cell RNA-seq data
.png){kind=icon}
OA
AI
DOI：10.1093/bib/bbac172.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Clustering analysis is widely used in single-cell ribonucleic acid (RNA)-sequencing (scRNA-seq) data to discover cell heterogeneity and cell states. While many clustering methods have been developed for scRNA-seq analysis, most of these methods require to provide the number of clusters. However, it is not easy to know the exact number of cell types in advance, and experienced determination is not always reliable. Here, we have developed ADClust, an automatic deep embedding clustering method for scRNA-seq data, which can accurately cluster cells without requiring a predefined number of clusters. Specifically, ADClust first obtains low-dimensional representation through pre-trained autoencoder and uses the representations to cluster cells into initial micro-clusters. The clusters are then compared in between by a statistical test, and similar micro-clusters are merged into larger clusters. According to the clustering, cell representations are updated so that each cell will be pulled toward centers of its assigned cluster and similar clusters, while cells are separated to keep distances between clusters. This is accomplished through jointly optimizing the carefully designed clustering and autoencoder loss functions. This merging process continues until convergence. ADClust was tested on 11 real scRNA-seq datasets and was shown to outperform existing methods in terms of both clustering performance and the accuracy on the number of the determined clusters. More importantly, our model provides high speed and scalability for large datasets.
Keyword:
deep embedded clustering
dip-test
estimating the number of cell clusters
single-cell clustering
single-cell RNA sequencing
期刊
IF:
7.7
论文数:
5.8K
被引数:
2.7W
机构
引用论文
Benchmarking single-cell RNA-sequencing protocols for cell atlas projects.png)
NATURE BIOTECHNOLOGY
OAAI
NATURE BIOTECHNOLOGYIF41.7
OAAI分享收藏MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing dataMultiK: 一种确定单细胞RNA测序数据中最佳簇数的自动化工具GENOME BIOLOGYOAAI
GENOME BIOLOGYIF9.4
OAAI分享收藏SAIC: an iterative clustering approach for analysis of single cell RNA-seq dataBMC GENOMICSOAAI
BMC GENOMICSIF3.7
OAAI分享收藏Genetic Polymorphism of 11 Allozyme Loci in Populations of Wall Lizards (Podarcis sp.) from the Iberian Peninsula and North Africa来自伊比利亚半岛和北非的壁蜥蜴 (Podarcis sp。) 种群中11个同工酶基因座的遗传多态性
PREAI
分享收藏Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysisGENOME BIOLOGYOAAI
GENOME BIOLOGYIF9.4
OAAI分享收藏CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq dataCIDR: 通过单细胞rna-seq数据的插补实现超快和准确的聚类GENOME BIOLOGYOAAI
GENOME BIOLOGYIF9.4
OAAI分享收藏
查看更多内容