An accurate and robust imputation method scImpute for single-cell RNA-seq data.
An accurate and robust imputation method scImpute for single-cell RNA-seq data.
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DOI：
10.1038/s41467-018-03405-7
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发表时间：
2018-03-08
影响因子：
16.6
通讯作者：
Li JJ
中科院分区：
文献类型：
--
作者：
Li WV;Li JJ
新兴的单细胞RNA测序(scRNA-seq)技术使得能够在更高的单细胞分辨率下研究转录图谱。ScRNA-seq数据分析因过多的零计数而变得复杂，即由于单个细胞内测序的信使核糖核酸数量较少而导致的所谓辍学。我们引入了scImpute，这是一种统计方法，可以准确而稳健地估计scRNA-seq数据中的丢失。ScImpute自动识别可能的退出，并且仅对这些值执行计算，而不会向REST数据引入新的偏差。ScImpute还检测离群点细胞，并将它们排除在计算之外。基于模拟和真实的人和小鼠scRNA-seq数据的评估表明，scImpute是恢复丢失所掩盖的转录组动力学的有效工具。ScImpute被证明可以识别可能的辍学，增强细胞亚群的聚集性，提高差异表达分析的准确性，并有助于基因表达动力学的研究。尽管在探索细胞异质性和基因表达的随机性方面得到了广泛的应用，但单细胞RNA-SEQ分析因过多的零计数(丢弃)而变得复杂。在这里，Li和Li开发了scImpute，用于统计scRNA-seq数据中的丢失。
The emerging single-cell RNA sequencing (scRNA-seq) technologies enable the investigation of transcriptomic landscapes at the single-cell resolution. ScRNA-seq data analysis is complicated by excess zero counts, the so-called dropouts due to low amounts of mRNA sequenced within individual cells. We introduce scImpute, a statistical method to accurately and robustly impute the dropouts in scRNA-seq data. scImpute automatically identifies likely dropouts, and only perform imputation on these values without introducing new biases to the rest data. scImpute also detects outlier cells and excludes them from imputation. Evaluation based on both simulated and real human and mouse scRNA-seq data suggests that scImpute is an effective tool to recover transcriptome dynamics masked by dropouts. scImpute is shown to identify likely dropouts, enhance the clustering of cell subpopulations, improve the accuracy of differential expression analysis, and aid the study of gene expression dynamics. Despite being widely performed in exploring cell heterogeneity and gene expression stochasticity, single cell RNA-seq analysis is complicated by excess zero counts (dropouts). Here, Li and Li develop scImpute for statistical imputation of dropouts in scRNA-seq data.
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综合的单细胞数据分析揭示了细胞特异性网络和新颖的共激活标记。
影响因子：
--
作者：
Ghazanfar S;Bisogni AJ;Ormerod JT;Lin DM;Yang JY
通讯作者：
Yang JY
影响因子：
14.9
作者：
Saliba AE;Westermann AJ;Gorski SA;Vogel J
通讯作者：
Vogel J
影响因子：
15.9
作者：
Novak, N;Tepel, C;Kraft, S
通讯作者：
Kraft, S
影响因子：
48
作者：
Kharchenko, Peter V.;Silberstein, Lev;Scadden, David T.
通讯作者：
Scadden, David T.
影响因子：
14.9
作者：
Blake JA;Eppig JT;Kadin JA;Richardson JE;Smith CL;Bult CJ;the Mouse Genome Database Group
通讯作者：
the Mouse Genome Database Group