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Robust integration of multiple single-cell RNA sequencing datasets using a single reference space

Nature Biotechnology volume 39, pages 877–884 (2021)Cite this article

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Abstract

In many biological applications of single-cell RNA sequencing (scRNA-seq), an integrated analysis of data from multiple batches or studies is necessary. Current methods typically achieve integration using shared cell types or covariance correlation between datasets, which can distort biological signals. Here we introduce an algorithm that uses the gene eigenvectors from a reference dataset to establish a global frame for integration. Using simulated and real datasets, we demonstrate that this approach, called Reference Principal Component Integration (RPCI), consistently outperforms other methods by multiple metrics, with clear advantages in preserving genuine cross-sample gene expression differences in matching cell types, such as those present in cells at distinct developmental stages or in perturbated versus control studies. Moreover, RPCI maintains this robust performance when multiple datasets are integrated. Finally, we applied RPCI to scRNA-seq data for mouse gut endoderm development and revealed temporal emergence of genetic programs helping establish the anterior–posterior axis in visceral endoderm.

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Fig. 1: Schematic illustration of data integration.
Fig. 2: Performance of RPCI in integrating simulated heterogeneous datasets.
Fig. 3: RPCI integration of semi-simulated datasets with diverse cell types and multiple perturbations.
Fig. 4: Performance of RPCI in integrating WT and ERR KO snRNA-seq datasets.
Fig. 5: Maintenance of correct cell type relationship in endoderm developmental trajectory in RPCI integration.

Data availability

All scRNA-seq datasets in this study were published previously, and their availabilities are described in Supplementary Table 2.

Code availability

The RISC is prepared as an R package and is available for free use via GitHub (https://github.com/bioinfoDZ/RISC). Codes for the analysis (and related source data) are provided in the Code Ocean (https://codeocean.com/capsule/9098032).

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Acknowledgements

We thank all the research groups that generated and shared the scRNA-seq data used in this study. We thank the members of the Zheng lab for valuable discussions, software testing and comments on the manuscript. We also acknowledge funding support from the National Institutes of Health (grants HL133120 to D.Z. and B.Z., HL153920 to D.Z., HD092944 to D.Z. and B.Z., and HD070454 to D.Z.).

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Authors and Affiliations

  1. Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA

    Yang Liu, Tao Wang, Bin Zhou & Deyou Zheng

  2. Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA

    Tao Wang

  3. Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA

    Bin Zhou

  4. Department of Medicine (Cardiology), Albert Einstein College of Medicine, Bronx, NY, USA

    Bin Zhou

  5. Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA

    Deyou Zheng

  6. Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA

    Deyou Zheng

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  1. Yang Liu
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  2. Tao Wang
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  4. Deyou Zheng
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Contributions

Y.L. and D.Z. conceived the algorithm and analysis. Y.L. performed the analyses. T.W. and B.Z. contributed to the methods or discussions. D.Z. supervised the study. All authors wrote the manuscript.

Corresponding author

Correspondence to Deyou Zheng.

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The authors declare no competing interests.

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Peer review information Nature Biotechnology thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Notes, Supplementary Methods and Supplementary Figs. 1–21.

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Supplementary Tables 1 and 2

Supplementary Table 1. Metric scores from pairwise integration and full integration of the semi-simulated data. Supplementary Table 2. List of real scRNA-seq datasets.

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Liu, Y., Wang, T., Zhou, B. et al. Robust integration of multiple single-cell RNA sequencing datasets using a single reference space. Nat Biotechnol 39, 877–884 (2021). https://doi.org/10.1038/s41587-021-00859-x

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