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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Single-cell RNA-seq data clustering: A survey with performance comparison study
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Single-cell RNA-seq data clustering: A survey with performance comparison study

机译：单细胞RNA-SEQ数据聚类：具有性能比较研究的调查

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摘要

Clustering analysis has been widely applied to single-cell RNA-sequencing (scRNA-seq) data to discover cell types and cell states. Algorithms developed in recent years have greatly helped the understanding of cellular heterogeneity and the underlying mechanisms of biological processes. However, these algorithms often use different techniques, were evaluated on different datasets and compared with some of their counterparts usually using different performance metrics. Consequently, there lacks an accurate and complete picture of their merits and demerits, which makes it difficult for users to select proper algorithms for analyzing their data. To fill this gap, we first do a review on the major existing scRNA-seq data clustering methods, and then conduct a comprehensive performance comparison among them from multiple perspectives. We consider 13 state of the art scRNA-seq data clustering algorithms, and collect 12 publicly available real scRNA-seq datasets from the existing works to evaluate and compare these algorithms. Our comparative study shows that the existing methods are very diverse in performance. Even the top-performance algorithms do not perform well on all datasets, especially those with complex structures. This suggests that further research is required to explore more stable, accurate, and efficient clustering algorithms for scRNA-seq data.
机译：聚类分析已被广泛应用于单细胞RNA测序（ScrNA-SEQ）数据，以发现细胞类型和细胞状态。近年来开发的算法极大地帮助了解细胞异质性和生物过程的潜在机制。然而，这些算法通常使用不同的技术，在不同的数据集上进行评估，并与通常使用不同性能度量的一些对应物进行比较。因此，缺乏其优点和缺点的准确性和完整的图片，这使得用户难以选择适当的算法来分析其数据。为了填补这一差距，我们首先对主要现有ScrNA-SEQ数据聚类方法进行审查，然后在多个视角下进行全面的性能比较。我们考虑了13个现有的SCRNA-SEQ数据聚类算法，并从现有的工程中收集12个公共SCRNA-SEQ数据集来评估和比较这些算法。我们的比较研究表明，现有的方法在性能方面非常多样化。即使是顶部性能算法也不会在所有数据集中表现良好，尤其是具有复杂结构的数据集。这表明需要进一步的研究来探索用于SCRNA-SEQ数据的更稳定，准确，有效的聚类算法。

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