scAce: an adaptive embedding and clustering method for single-cell gene expression data.
scAce: an adaptive embedding and clustering method for single-cell gene expression data.
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DOI：
10.1093/bioinformatics/btad546
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发表时间：
2023-09-02
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自单细胞RNA测序（scRNA-seq）技术发展以来，单细胞基因表达数据的聚类分析已成为区分细胞类型和鉴定新细胞类型的重要工具。尽管许多方法可用于scRNA-seq聚类分析，但它们中的大多数受到对预定簇数的要求或对所选择的初始簇分配的依赖性的限制。在这篇文章中，我们提出了一个自适应嵌入和聚类方法命名为scAce，它构建了一个变分自动编码器，同时学习细胞嵌入和集群分配。在scAce方法中，我们开发了一种自适应集群合并方法，可以在不需要提前估计集群数量的情况下实现改进的集群结果。此外，scAce提供了一个执行聚类增强的选项，可以根据其他方法的先前聚类结果更新和增强聚类分配。基于模拟和真实的数据集的计算分析，我们证明了scAce优于scRNA-seq数据的最新聚类方法，并实现了更好的聚类准确性和鲁棒性。scAce包是在python 3.8中实现的，可以从https://github.com/sldyns/scAce免费获得。
Since the development of single-cell RNA sequencing (scRNA-seq) technologies, clustering analysis of single-cell gene expression data has been an essential tool for distinguishing cell types and identifying novel cell types. Even though many methods have been available for scRNA-seq clustering analysis, the majority of them are constrained by the requirement on predetermined cluster numbers or the dependence on selected initial cluster assignment. In this article, we propose an adaptive embedding and clustering method named scAce, which constructs a variational autoencoder to simultaneously learn cell embeddings and cluster assignments. In the scAce method, we develop an adaptive cluster merging approach which achieves improved clustering results without the need to estimate the number of clusters in advance. In addition, scAce provides an option to perform clustering enhancement, which can update and enhance cluster assignments based on previous clustering results from other methods. Based on computational analysis of both simulated and real datasets, we demonstrate that scAce outperforms state-of-the-art clustering methods for scRNA-seq data, and achieves better clustering accuracy and robustness. The scAce package is implemented in python 3.8 and is freely available from https://github.com/sldyns/scAce.
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