College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China.
College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, Hunan 410003, China.
Brief Bioinform. 2022 Jan 17;23(1). doi: 10.1093/bib/bbab442.
The tremendous progress of single-cell sequencing technology has given researchers the opportunity to study cell development and differentiation processes at single-cell resolution. Assay of Transposase-Accessible Chromatin by deep sequencing (ATAC-seq) was proposed for genome-wide analysis of chromatin accessibility. Due to technical limitations or other reasons, dropout events are almost a common occurrence for extremely sparse single-cell ATAC-seq data, leading to confusion in downstream analysis (such as clustering). Although considerable progress has been made in the estimation of scRNA-seq data, there is currently no specific method for the inference of dropout events in single-cell ATAC-seq data. In this paper, we select several state-of-the-art scRNA-seq imputation methods (including MAGIC, SAVER, scImpute, deepImpute, PRIME, bayNorm and knn-smoothing) in recent years to infer dropout peaks in scATAC-seq data, and perform a systematic evaluation of these methods through several downstream analyses. Specifically, we benchmarked these methods in terms of correlation with meta-cell, clustering, subpopulations distance analysis, imputation performance for corruption datasets, identification of TF motifs and computation time. The experimental results indicated that most of the imputed peaks increased the correlation with the reference meta-cell, while the performance of different methods on different datasets varied greatly in different downstream analyses, thus should be used with caution. In general, MAGIC performed better than the other methods most consistently across all assessments. Our source code is freely available at https://github.com/yueyueliu/scATAC-master.
单细胞测序技术的巨大进展使研究人员有机会在单细胞分辨率下研究细胞发育和分化过程。转座酶可及染色质测序（ATAC-seq）分析方法被提出用于全基因组染色质可及性分析。由于技术限制或其他原因，在极其稀疏的单细胞 ATAC-seq 数据中，缺失事件几乎是常见的，这导致下游分析（如聚类）出现混乱。尽管在 scRNA-seq 数据的估计方面已经取得了相当大的进展，但目前还没有针对单细胞 ATAC-seq 数据中缺失事件推断的特定方法。在本文中，我们选择了近年来几种最先进的 scRNA-seq 插补方法（包括 MAGIC、SAVER、scImpute、deepImpute、PRIME、bayNorm 和 knn-smoothing）来推断 scATAC-seq 数据中的缺失峰，并通过几种下游分析对这些方法进行了系统评估。具体来说，我们根据与参考元细胞、聚类、亚群距离分析、腐败数据集插补性能、TF 基序识别和计算时间的相关性对这些方法进行了基准测试。实验结果表明，大多数插补峰增加了与参考元细胞的相关性，而不同方法在不同数据集上的性能在不同的下游分析中差异很大，因此应谨慎使用。总体而言，MAGIC 在所有评估中表现出了比其他方法更好的一致性。我们的源代码可在 https://github.com/yueyueliu/scATAC-master 上免费获取。
