CosTaL: an accurate and scalable graph-based clustering algorithm for high-dimensional single-cell data analysis
CosTaL: an accurate and scalable graph-based clustering algorithm for high-dimensional single-cell data analysis
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DOI：
10.1093/bib/bbad157
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发表时间：
2023-05-05
影响因子：
9.5
通讯作者：
Arriaga, Edgar A.
中科院分区：
文献类型：
--
作者：
Li, Yijia;Nguyen, Jonathan;Arriaga, Edgar A.
为了分析大型多维单细胞数据集，本文描述了一种基于余弦的谷本相似度精细图的Leiden算法（CosTaL）社区检测方法。CosTaL是一种基于图的聚类方法，它将具有高维特征的单元格转换成加权k-最近邻（kNN）图。单元格由图的顶点表示，而图中两个顶点之间的边表示两个单元格之间的密切关系。具体而言，CosTaL使用余弦相似度构建精确的kNN图，并使用谷本系数作为细化策略重新加权边缘，以提高聚类的有效性。我们证明，与其他最先进的基于图的聚类方法（包括PhenoGraph， Scanpy和PARC）相比，使用六种不同的评估指标，CosTaL通常在七个基准细胞仪数据集和六个单细胞rna测序数据集上获得同等或更高的有效性得分。综合评价指标表明，Costal在小数据集上具有较高的效率，在大数据集上具有良好的可扩展性，有利于大规模分析。
With the aim of analyzing large-sized multidimensional single-cell datasets, we are describing a method for Cosine-based Tanimoto similarity-refined graph for community detection using Leiden's algorithm (CosTaL). As a graph-based clustering method, CosTaL transforms the cells with high-dimensional features into a weighted k-nearest-neighbor (kNN) graph. The cells are represented by the vertices of the graph, while an edge between two vertices in the graph represents the close relatedness between the two cells. Specifically, CosTaL builds an exact kNN graph using cosine similarity and uses the Tanimoto coefficient as the refining strategy to re-weight the edges in order to improve the effectiveness of clustering. We demonstrate that CosTaL generally achieves equivalent or higher effectiveness scores on seven benchmark cytometry datasets and six single-cell RNA-sequencing datasets using six different evaluation metrics, compared with other state-of-the-art graph-based clustering methods, including PhenoGraph, Scanpy and PARC. As indicated by the combined evaluation metrics, Costal has high efficiency with small datasets and acceptable scalability for large datasets, which is beneficial for large-scale analysis.