SCMcluster: a high-precision cell clustering algorithm integrating marker gene set with single-cell RNA sequencing data.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2023-07-17 DOI:10.1093/bfgp/elad004
Hao Wu, Haoru Zhou, Bing Zhou, Meili Wang
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Abstract

Single-cell clustering is the most significant part of single-cell RNA sequencing (scRNA-seq) data analysis. One main issue facing the scRNA-seq data is noise and sparsity, which poses a great challenge for the advance of high-precision clustering algorithms. This study adopts cellular markers to identify differences between cells, which contributes to feature extraction of single cells. In this work, we propose a high-precision single-cell clustering algorithm-SCMcluster (single-cell cluster using marker genes). This algorithm integrates two cell marker databases(CellMarker database and PanglaoDB database) with scRNA-seq data for feature extraction and constructs an ensemble clustering model based on the consensus matrix. We test the efficiency of this algorithm and compare it with other eight popular clustering algorithms on two scRNA-seq datasets derived from human and mouse tissues, respectively. The experimental results show that SCMcluster outperforms the existing methods in both feature extraction and clustering performance. The source code of SCMcluster is available for free at https://github.com/HaoWuLab-Bioinformatics/SCMcluster.

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SCMcluster:一种整合标记基因集和单细胞RNA测序数据的高精度细胞聚类算法。
单细胞聚类是单细胞RNA测序(scRNA-seq)数据分析中最重要的部分。scRNA-seq数据面临的一个主要问题是噪声和稀疏性,这对高精度聚类算法的发展提出了很大的挑战。本研究采用细胞标记来识别细胞间的差异,有助于提取单细胞的特征。在这项工作中,我们提出了一种高精度的单细胞聚类算法- scmcluster(使用标记基因的单细胞聚类)。该算法将两个细胞标记数据库(CellMarker数据库和PanglaoDB数据库)与scRNA-seq数据集成在一起进行特征提取,并构建基于共识矩阵的集成聚类模型。我们测试了该算法的效率,并将其与其他八种流行的聚类算法在分别来自人类和小鼠组织的两个scRNA-seq数据集上进行了比较。实验结果表明,SCMcluster在特征提取和聚类性能上都优于现有方法。SCMcluster的源代码可以在https://github.com/HaoWuLab-Bioinformatics/SCMcluster上免费获得。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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