IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-021-27729-z.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-021-27729-z
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-021-27729-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yichuan Cao & Xiamiao Zhao & Songming Tang & Qun Jiang & Sijie Li & Siyu Li & Shengquan Chen, 2024. "scButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    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. 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.
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Ethan Bahl & Snehajyoti Chatterjee & Utsav Mukherjee & Muhammad Elsadany & Yann Vanrobaeys & Li-Chun Lin & Miriam McDonough & Jon Resch & K. Peter Giese & Ted Abel & Jacob J. Michaelson, 2024. "Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    8. Ziqi Zhang & Xinye Zhao & Mehak Bindra & Peng Qiu & Xiuwei Zhang, 2024. "scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    9. Nicolae Sapoval & Amirali Aghazadeh & Michael G. Nute & Dinler A. Antunes & Advait Balaji & Richard Baraniuk & C. J. Barberan & Ruth Dannenfelser & Chen Dun & Mohammadamin Edrisi & R. A. Leo Elworth &, 2022. "Current progress and open challenges for applying deep learning across the biosciences," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    10. 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.
    11. 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.
    12. Ian Covert & Rohan Gala & Tim Wang & Karel Svoboda & Uygar Sümbül & Su-In Lee, 2023. "Predictive and robust gene selection for spatial transcriptomics," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    13. Minhui Chen & Andy Dahl, 2024. "A robust model for cell type-specific interindividual variation in single-cell RNA sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    14. Xin Tang & Jiawei Zhang & Yichun He & Xinhe Zhang & Zuwan Lin & Sebastian Partarrieu & Emma Bou Hanna & Zhaolin Ren & Hao Shen & Yuhong Yang & Xiao Wang & Na Li & Jie Ding & Jia Liu, 2023. "Explainable multi-task learning for multi-modality biological data analysis," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    15. Lulu Shang & Xiang Zhou, 2022. "Spatially aware dimension reduction for spatial transcriptomics," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
    16. Lucy Xia & Christy Lee & Jingyi Jessica Li, 2024. "Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    17. Vidhya M. Ravi & Nicolas Neidert & Paulina Will & Kevin Joseph & Julian P. Maier & Jan Kückelhaus & Lea Vollmer & Jonathan M. Goeldner & Simon P. Behringer & Florian Scherer & Melanie Boerries & Marie, 2022. "T-cell dysfunction in the glioblastoma microenvironment is mediated by myeloid cells releasing interleukin-10," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    18. Andrea Riba & Attila Oravecz & Matej Durik & Sara Jiménez & Violaine Alunni & Marie Cerciat & Matthieu Jung & Céline Keime & William M. Keyes & Nacho Molina, 2022. "Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    19. Yasa Baig & Helena R. Ma & Helen Xu & Lingchong You, 2023. "Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    20. Yuge Wang & Hongyu Zhao, 2022. "Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders," PLOS Computational Biology, Public Library of Science, vol. 18(4), pages 1-31, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-021-27729-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.