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An accurate and robust imputation method scImpute for single-cell RNA-seq data

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  • Wei Vivian Li

    (University of California)

  • Jingyi Jessica Li

    (University of California
    University of California)

Abstract

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.

Suggested Citation

  • Wei Vivian Li & Jingyi Jessica Li, 2018. "An accurate and robust imputation method scImpute for single-cell RNA-seq data," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-03405-7
    DOI: 10.1038/s41467-018-03405-7
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    Citations

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    Cited by:

    1. Hui Li & Cory R. Brouwer & Weijun Luo, 2022. "A universal deep neural network for in-depth cleaning of single-cell RNA-Seq data," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Songming Tang & Xuejian Cui & Rongxiang Wang & Sijie Li & Siyu Li & Xin Huang & Shengquan Chen, 2024. "scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    3. Xianke Xiang & Yao He & Zemin Zhang & Xuerui Yang, 2024. "Interrogations of single-cell RNA splicing landscapes with SCASL define new cell identities with physiological relevance," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    4. Kaiwen Wang & Yuqiu Yang & Fangjiang Wu & Bing Song & Xinlei Wang & Tao Wang, 2023. "Comparative analysis of dimension reduction methods for cytometry by time-of-flight data," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    5. Jing Qi & Yang Zhou & Zicen Zhao & Shuilin Jin, 2021. "SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-20, June.
    6. Zhijian Li & Christoph Kuppe & Susanne Ziegler & Mingbo Cheng & Nazanin Kabgani & Sylvia Menzel & Martin Zenke & Rafael Kramann & Ivan G. Costa, 2021. "Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    7. Md Tauhidul Islam & Jen-Yeu Wang & Hongyi Ren & Xiaomeng Li & Masoud Badiei Khuzani & Shengtian Sang & Lequan Yu & Liyue Shen & Wei Zhao & Lei Xing, 2022. "Leveraging data-driven self-consistency for high-fidelity gene expression recovery," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    8. Benjamin L. Walker & Qing Nie, 2023. "NeST: nested hierarchical structure identification in spatial transcriptomic data," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    9. George C. Linderman & Jun Zhao & Manolis Roulis & Piotr Bielecki & Richard A. Flavell & Boaz Nadler & Yuval Kluger, 2022. "Zero-preserving imputation of single-cell RNA-seq data," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    10. Lingfei Wang, 2021. "Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    11. Hyun Kim & Won Chang & Seok Joo Chae & Jong-Eun Park & Minseok Seo & Jae Kyoung Kim, 2024. "scLENS: data-driven signal detection for unbiased scRNA-seq data analysis," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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