scAce:单细胞基因表达数据的自适应嵌入和聚类方法。

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Xinwei He, Kun Qian, Ziqian Wang, Shirou Zeng, Hongwei Li, Wei Vivian Li
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引用次数: 0

摘要

动因: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一直是区分细胞类型和识别新型细胞类型的重要工具。尽管目前已有许多用于 scRNA-seq 聚类分析的方法,但大多数方法都受限于对预定聚类数量的要求或对选定的初始聚类分配的依赖:在本文中,我们提出了一种名为 scAce 的自适应嵌入和聚类方法,它构建了一个变异自动编码器来同时学习细胞嵌入和聚类分配。在 scAce 方法中,我们开发了一种自适应聚类合并方法,无需提前估计聚类数量,就能获得更好的聚类结果。此外,scAce 还提供了执行聚类增强的选项,可以根据其他方法的聚类结果更新和增强聚类分配。基于对模拟数据集和真实数据集的计算分析,我们证明了在 scRNA-seq 数据方面,scAce 优于最先进的聚类方法,并实现了更好的聚类准确性和鲁棒性:scAce 软件包由 python 3.8 实现,可从 https://github.com/sldyns/scAce 免费获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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.

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.

Motivation: 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.

Results: 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.

Availability and implementation: The scAce package is implemented in python 3.8 and is freely available from https://github.com/sldyns/scAce.

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来源期刊
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
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