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Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis.
Genome Biology ( IF 12.3 ) Pub Date : 2019-12-10 , DOI: 10.1186/s13059-019-1898-6
Shiquan Sun 1, 2 , Jiaqiang Zhu 2 , Ying Ma 2 , Xiang Zhou 2, 3
Affiliation  

BACKGROUND Dimensionality reduction is an indispensable analytic component for many areas of single-cell RNA sequencing (scRNA-seq) data analysis. Proper dimensionality reduction can allow for effective noise removal and facilitate many downstream analyses that include cell clustering and lineage reconstruction. Unfortunately, despite the critical importance of dimensionality reduction in scRNA-seq analysis and the vast number of dimensionality reduction methods developed for scRNA-seq studies, few comprehensive comparison studies have been performed to evaluate the effectiveness of different dimensionality reduction methods in scRNA-seq. RESULTS We aim to fill this critical knowledge gap by providing a comparative evaluation of a variety of commonly used dimensionality reduction methods for scRNA-seq studies. Specifically, we compare 18 different dimensionality reduction methods on 30 publicly available scRNA-seq datasets that cover a range of sequencing techniques and sample sizes. We evaluate the performance of different dimensionality reduction methods for neighborhood preserving in terms of their ability to recover features of the original expression matrix, and for cell clustering and lineage reconstruction in terms of their accuracy and robustness. We also evaluate the computational scalability of different dimensionality reduction methods by recording their computational cost. CONCLUSIONS Based on the comprehensive evaluation results, we provide important guidelines for choosing dimensionality reduction methods for scRNA-seq data analysis. We also provide all analysis scripts used in the present study at www.xzlab.org/reproduce.html.

中文翻译:

用于单细胞RNA序列分析的降维方法的准确性,鲁棒性和可扩展性。

背景技术降维是单细胞RNA测序(scRNA-seq)数据分析许多领域必不可少的分析组件。适当的降维可以实现有效的噪声去除,并促进许多下游分析,包括细胞聚类和谱系重建。不幸的是,尽管降维在scRNA-seq分析中至关重要,并且为scRNA-seq研究开发了大量降维方法,但很少进行全面的比较研究来评估不同降维方法在scRNA-seq中的有效性。结果我们旨在通过对scRNA-seq研究中各种常用的降维方法进行比较评估来填补这一关键的知识空白。具体来说,我们在涵盖一系列测序技术和样本量的30个可公开获得的scRNA-seq数据集上比较了18种不同的降维方法。我们评估不同维数缩减方法的性能,以恢复其原始表达矩阵的特征为邻域保留能力,以及以其准确性和鲁棒性来进行细胞聚类和谱系重建。我们还通过记录不同的降维方法的计算成本来评估它们的计算可伸缩性。结论基于综合评估结果,我们为选择用于SCRNA-seq数据分析的降维方法提供了重要指导。我们还提供了本研究中使用的所有分析脚本,网址为www.xzlab.org/reproduce.html。
更新日期:2019-12-10
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