当前位置: X-MOL 学术J. Bioinform. Comput. Biol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Single-cell RNA-seq data clustering: A survey with performance comparison study
Journal of Bioinformatics and Computational Biology ( IF 1 ) Pub Date : 2020-08-14 , DOI: 10.1142/s0219720020400053
Ruiyi Li 1 , Jihong Guan 1 , Shuigeng Zhou 2
Affiliation  

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.

中文翻译:

单细胞 RNA-seq 数据聚类:性能比较研究的调查

聚类分析已广泛应用于单细胞 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理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指标的一些对应物进行比较。因此,对它们的优缺点缺乏准确和完整的描述,这使得用户难以选择合适的算法来分析他们的数据。为了填补这一空白,我们首先对现有的主要 scRNA-seq 数据聚类方法进行了回顾,然后从多个角度对其进行了全面的性能比较。我们考虑了 13 种最先进的 scRNA-seq 数据聚类算法,并从现有作品中收集了 12 个公开可用的真实 scRNA-seq 数据集,以评估和比较这些算法。我们的比较研究表明,现有方法在性能上非常多样化。即使是性能最好的算法也不能在所有数据集上表现良好,尤其是那些具有复杂结构的数据集。这表明需要进一步的研究来探索更稳定、准确和高效的 scRNA-seq 数据聚类算法。即使是性能最好的算法也不能在所有数据集上表现良好,尤其是那些具有复杂结构的数据集。这表明需要进一步的研究来探索更稳定、准确和高效的 scRNA-seq 数据聚类算法。即使是性能最好的算法也不能在所有数据集上表现良好,尤其是那些具有复杂结构的数据集。这表明需要进一步的研究来探索更稳定、准确和高效的 scRNA-seq 数据聚类算法。
更新日期:2020-08-14
down
wechat
bug