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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All scRNA-seq datasets in this study were published previously, and their availabilities are described in Supplementary Table 2.
Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).
Nawy, T. Single-cell sequencing. Nat. Methods 11, 18 (2014).
Wang, Y. & Navin, N. E. Advances and applications of single-cell sequencing technologies. Mol. Cell 58, 598–609 (2015).
Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308 (2018).
Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).
Fan, X. et al. Spatial transcriptomic survey of human embryonic cerebral cortex by single-cell RNA-seq analysis. Cell Res. 28, 730–745 (2018).
Wang, J. X. et al. Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons. Genome Res. 27, 1783–1794 (2017).
Davie, K. et al. A single-cell transcriptome atlas of the aging Drosophila brain. Cell 174, 982–998 (2018).
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).
Hie, B., Bryson, B. & Berger, B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37, 685–691 (2019).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Lin, Y. et al. scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets. Proc. Natl Acad. Sci. USA 116, 9775–9784 (2019).
Lotfollahi, M., Wolf, F. A. & Theis, F. J. scGen predicts single-cell perturbation responses. Nat. Methods 16, 715–721 (2019).
Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S. & Vert, J. P. A general and flexible method for signal extraction from single-cell RNA-seq data. Nat. Commun. 9, 284 (2018).
Shaham, U. et al. Removal of batch effects using distribution-matching residual networks. Bioinformatics 33, 2539–2546 (2017).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
Wang, T. et al. BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes. Genome Biol. 20, 165 (2019).
Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887 (2019).
Zhang, F., Wu, Y. & Tian, W. A novel approach to remove the batch effect of single-cell data. Cell Discov. 5, 46 (2019).
Tran, H. T. N. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020).
Jolliffe, I. T. & Cadima, J. Principal component analysis: a review and recent developments. Philos. Trans. A Math. Phys. Eng. Sci. 374, 20150202 (2016).
Jolliffe, I. T. Principal Component Analysis (Springer, 2011).
Zhang, X., Xu, C. & Yosef, N. Simulating multiple faceted variability in single cell RNA sequencing. Nat. Commun. 10, 2611 (2019).
Zappia, L., Phipson, B. & Oshlack, A. Splatter: simulation of single-cell RNA sequencing data. Genome Biol. 18, 174 (2017).
Scrucca, L., Fop, M., Murphy, T. B. & Raftery, A. E. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R J 8, 289–317 (2016).
Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987).
Buttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A. & Theis, F. J. A test metric for assessing single-cell RNA-seq batch correction. Nat. Methods 16, 43–49 (2019).
Villani, A. C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573 (2017).
Hu, P. et al. Single-nucleus transcriptomic survey of cell diversity and functional maturation in postnatal mammalian hearts. Genes Dev. 32, 1344–1357 (2018).
Liu, Y., Singh, V. K. & Zheng, D. Stereo3D: using stereo images to enrich 3D visualization. Bioinformatics 36, 4189–4190 (2020).
Nowotschin, S. et al. The emergent landscape of the mouse gut endoderm at single-cell resolution. Nature 569, 361–367 (2019).
Arnold, S. J. & Robertson, E. J. Making a commitment: cell lineage allocation and axis patterning in the early mouse embryo. Nat. Rev. Mol. Cell Biol. 10, 91–103 (2009).
Nowotschin, S., Hadjantonakis, A. K. & Campbell, K. The endoderm: a divergent cell lineage with many commonalities. Development 146, dev150920 (2019).
Stuckey, D. W., Di Gregorio, A., Clements, M. & Rodriguez, T. A. Correct patterning of the primitive streak requires the anterior visceral endoderm. PLoS ONE 6, e17620 (2011).
Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).
Pepe-Mooney, B. J. et al. Single-cell analysis of the liver epithelium reveals dynamic heterogeneity and an essential role for YAP in homeostasis and regeneration. Cell Stem Cell 25, 23–38 (2019).
Hill, M. C. et al. A cellular atlas of Pitx2-dependent cardiac development. Development 146, dev180398 (2019).
Gordon, S. R. et al. PD-1 expression by tumour-associated macrophages inhibits phagocytosis and tumour immunity. Nature 545, 495–499 (2017).
Savas, P. et al. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat. Med. 24, 986–993 (2018).
Yost, K. E. et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25, 1251–1259 (2019).
Ding, J. et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat. Biotechnol. 38, 737–746 (2020).
Grun, D. et al. De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell 19, 266–277 (2016).
Muraro, M. J. et al. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 3, 385–394 (2016).
Segerstolpe, A. et al. Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab. 24, 593–607 (2016).
Wang, Y. J. et al. Single-cell transcriptomics of the human endocrine pancreas. Diabetes 65, 3028–3038 (2016).
Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360 (2016).
Wolf, F. A. et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 20, 59 (2019).
Giraddi, R. R. et al. Single-cell transcriptomes distinguish stem cell state changes and lineage specification programs in early mammary gland development. Cell Rep. 24, 1653–1666 (2018).
Maaten, L. V. D. Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 3221–3245 (2014).
Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38 (2018).
Alter, O., Brown, P. O. & Botstein, D. Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl Acad. Sci. USA 97, 10101–10106 (2000).
Kolde, R. pheatmap: Pretty Heatmaps https://rdrr.io/cran/pheatmap/ (2019).
Zwiener, I., Frisch, B. & Binder, H. Transforming RNA-seq data to improve the performance of prognostic gene signatures. PLoS ONE 9, e85150 (2014).
Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
McCarthy, D. J., Campbell, K. R., Lun, A. T. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).
Lun, A. T., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).
Bacher, R. et al. SCnorm: robust normalization of single-cell RNA-seq data. Nat. Methods 14, 584–586 (2017).
R Core Team. R: A Language and Environment for Statistical Computing https://www.R-project.org/ (2019).
Koopmans, L. H., Owen, D. B. & Rosenblatt, J. I. Confidence intervals for the coefficient of variation for the normal and log normal distributions. Biometrika 51, 25–32 (1964).
Ver Hoef, J. M. & Boveng, P. L. Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data? Ecology 88, 2766–2772 (2007).
Gonzalez, I., Déjean, S., Martin, P. & Baccini, A. CCA: an R package to extend canonical correlation analysis. J. Stat. Softw. 23, 14 (2008).
Witten, D. M., Tibshirani, R. & Hastie, T. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10, 515–534 (2009).
Wooldridge, J.M. Introductory Econometrics: A Modern Approach (Cengage, 2018)
Lambert, D. Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics 34, 1–14 (1992).
Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster-analysis. J. Comput. Appl. Math. 20, 53–65 (1987).
Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K. cluster: Cluster Analysis Basics and Extensions https://cran.r-project.org/package=cluster (2019).
Venables, W.N., Ripley, B.D. & Venables, W.N. Modern Applied Statistics with S (Springer, 2002).
Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).
Korthauer, K. D. et al. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biol. 17, 222 (2016).
Nabavi, S., Schmolze, D., Maitituoheti, M., Malladi, S. & Beck, A. H. EMDomics: a robust and powerful method for the identification of genes differentially expressed between heterogeneous classes. Bioinformatics 32, 533–541 (2016).
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.).
The authors declare no competing interests.
Peer review information Nature Biotechnology thanks the anonymous reviewers for their contribution to the peer review of this work.
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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 (2021). https://doi.org/10.1038/s41587-021-00859-x