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Zero-preserving imputation of single-cell RNA-seq data

Author

Listed:
  • George C. Linderman

    (Yale University)

  • Jun Zhao

    (Yale University)

  • Manolis Roulis

    (Yale University)

  • Piotr Bielecki

    (Yale University
    Celsius Therapeutics)

  • Richard A. Flavell

    (Yale University
    Yale University School of Medicine)

  • Boaz Nadler

    (Weizmann Institute of Science)

  • Yuval Kluger

    (Yale University
    Yale University
    Yale University)

Abstract

A key challenge in analyzing single cell RNA-sequencing data is the large number of false zeros, where genes actually expressed in a given cell are incorrectly measured as unexpressed. We present a method based on low-rank matrix approximation which imputes these values while preserving biologically non-expressed genes (true biological zeros) at zero expression levels. We provide theoretical justification for this denoising approach and demonstrate its advantages relative to other methods on simulated and biological datasets.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-021-27729-z
    DOI: 10.1038/s41467-021-27729-z
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    References listed on IDEAS

    as
    1. Gökcen Eraslan & Lukas M. Simon & Maria Mircea & Nikola S. Mueller & Fabian J. Theis, 2019. "Single-cell RNA-seq denoising using a deep count autoencoder," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    2. 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.
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    Cited by:

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    2. Dorota Zawada & Jessica Kornherr & Anna B. Meier & Gianluca Santamaria & Tatjana Dorn & Monika Nowak-Imialek & Daniel Ortmann & Fangfang Zhang & Mark Lachmann & Martina Dreßen & Mariaestela Ortiz & Vi, 2023. "Retinoic acid signaling modulation guides in vitro specification of human heart field-specific progenitor pools," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    3. Hao Chen & Frederick J. King & Bin Zhou & Yu Wang & Carter J. Canedy & Joel Hayashi & Yang Zhong & Max W. Chang & Lars Pache & Julian L. Wong & Yong Jia & John Joslin & Tao Jiang & Christopher Benner , 2024. "Drug target prediction through deep learning functional representation of gene signatures," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    4. Zhenchao Tang & Guanxing Chen & Shouzhi Chen & Jianhua Yao & Linlin You & Calvin Yu-Chian Chen, 2024. "Modal-nexus auto-encoder for multi-modality cellular data integration and imputation," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    5. Shiman Zuo & Yuxin Wang & Hanjing Bao & Zehui Zhang & Nanfei Yang & Meng Jia & Qing Zhang & Ani Jian & Rong Ji & Lidan Zhang & Yan Lu & Yahong Huang & Pingping Shen, 2024. "Lipid synthesis, triggered by PPARγ T166 dephosphorylation, sustains reparative function of macrophages during tissue repair," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    6. 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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