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Integrating single-cell transcriptomic data across different conditions, technologies, and species
Open accessPublished version of a preprint
Abstract
Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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“identified five methods commonly in use: the default Seurat [19] FindMarkers() function based on the Wilcoxon test (Seurat-Wilcox), MIMOSCA [1], a t-test on the library-size-normalized expressions [10], MAST [20], and a Kolmogorov-Smirnov (KS) test on the library-size-normalized expressions [21]”
From Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection (2024) · cites this paper as [19]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Batch effects were corrected with the FindIntegrationAnchor() and the IntegrateData() function in the reciprocal PCA approach of the Seurat workflow.”
From TBX3 is dynamically expressed in pancreatic organogenesis and fine-tunes regeneration (2023) · cites this paper as [77]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Canonical correlation analysis (Butler et al., 2018) demonstrated that tumor KCs broadly recapitulated the normal (basal, cycling, and differentiating) subpopulations, with several shared marker genes across normal skin and cSCC”
From Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma (2020) · cites this paper as Butler et al., 2018
The cited source says what the claim attributes to it.
In the citing paper
“the hdWGCNA R package extends the data structures and functionality of the widely used Seurat package,5,6,7”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [5]
The cited source says what the claim attributes to it.
In the citing paper
“To maximize usability among the genomics community, the hdWGCNA R package extends the data structures and functionality of the widely used Seurat package,5,6,7 and we developed an extensive documentation website for hdWGCNA demonstrating its use on new datasets.”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [5]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Upregulation of CEBPB and CEBPD ( Fig. 1e left, Supplementary Fig. 2d, Supplementary Table 3 ), implicated in emergency granulopoiesis 30 and granulopoiesis under cellular stress 31 respectively, suggested a differentiation state into the granulocytic lineage.”
From Type 1 interferon perturbates clonal competition by reshaping human blood development (2026) · cites this paper as [31]
Experimental — accuracy not yet measuredIn the citing paper
“Of note, all samples were processed simultaneously in the same experiment and thus sample origin was not regressed out (Butler et al., 2018).”
From Genome-wide CRISPR Screens in Primary Human T Cells Reveal Key Regulators of Immune Function (2018) · cites this paper as Butler et al., 2018
In the citing paper
“The feature deviation matrix including unperturbed and CRISPR knockout cells was first processed using Seruat 2.0.1 (Butler et al., 2018) to regress out plate andexperiment batch effects.”
From Coupled Single-Cell CRISPR Screening and Epigenomic Profiling Reveals Causal Gene Regulatory Networks (2019) · cites this paper as Butler et al., 2018
In the citing paper
“Of note, all samples were processed simultaneously in the same experiment and thus sample origin was not regressed out (Butler et al., 2018).”
From Pooled Knockin Targeting for Genome Engineering of Cellular Immunotherapies (2020) · cites this paper as Butler et al., 2018
In the citing paper
“Increasingly sophisticated experimental approaches have allowed discovery of significant diversity and functional heterogeneity within the fibroblast population during organ fibrosis22–25 .”
From Fibrosis: from mechanisms to medicines (2020) · cites this paper as [25]
In the citing paper
“Many unsupervised scRNA-seq clustering methods have been proposed2–4, which are followed by time-consuming and labor-costly annotations5.”
From Transformer for one stop interpretable cell type annotation (2023) · cites this paper as [2]
In the citing paper
“the most widely used data alignment approaches (Butler et al., 2018; Haghverdi et al., 2018; Johnson et al., 2007; Risso et al., 2014) implicitly assume that the differences between datasets arise entirely from technical variation and attempt to eliminate them, or map datasets into a completely shared latent space using dimensions of maximum correlation (Butler et al., 2018).”
From Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity (2019) · cites this paper as Butler et al., 2018
In the citing paper
“For the PBMC and pancreas datasets, we reproduced the analyses as described by Butler et al. and subsequent Seurat tutorials (Butler et al., 2018).”
From Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity (2019) · cites this paper as Butler et al., 2018
In the citing paper
“To remove any potential batch effect between saline and cocaine samples, we run the CCA analysis17.”
From Cell type-specific transcriptional programs in mouse prefrontal cortex during adolescence and addiction (2019) · cites this paper as [17]
In the citing paper
“We thus first used canonical correlation analysis (CCA)17 to find a projection space that maximize the correlation between the two samples so that batch effect can be removed.”
From Cell type-specific transcriptional programs in mouse prefrontal cortex during adolescence and addiction (2019) · cites this paper as [17]
In the citing paper
“Popular platforms such as Seurat (Butler et al, 2018), Scater (McCarthy et al, 2017), or Scanpy (Wolf et al, 2018) provide integrated environments to develop pipelines and contain large analysis toolboxes. However, out of necessity these platforms limit themselves to tools developed in their respective programming languages.”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Butler et al, 2018
In the citing paper
“There is currently no consensus on whether or not to perform normalization over genes.”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Butler et al, 2018
In the citing paper
“While the popular Seurat tutorials (Butler et al, 2018) generally apply gene scaling, the authors of the Slingshot method opt against scaling over genes in their tutorial (Street et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Butler et al, 2018
In the citing paper
“Data integration methods such as Canonical Correlation Analysis (CCA; Butler et al, 2018), Mutual Nearest Neighbours (MNN; Haghverdi et al, 2018), Scanorama (preprint: Hie et al, 2018), RISC (preprint: Liu et al, 2018), scGen (preprint: Lotfollahi et al, 2018), LIGER (preprint: Welch et al, 2018), BBKNN (preprint: Park et al, 2018), and Harmony (preprint: Korsunsky et al, 2018) have been developed”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Butler et al, 2018
In the citing paper
“Currently available platforms exist on the command line in R (McCarthy et al, 2017; Butler et al, 2018) or Python (Wolf et al, 2018), and as local applications (Patel, 2018; preprint: Scholz et al, 2018) or Web servers (Gardeux et al, 2017; Zhu et al, 2017) with graphical user interfaces (GUIs).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Butler et al, 2018
In the citing paper
“Among command line platforms, Scater (McCarthy et al, 2017) and Seurat (Butler et al, 2018) easily interface with the large variety of analysis tools available via the R Bioconductor project (Huber et al, 2015).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Butler et al, 2018
In the citing paper
“It appears that aging-driven down-regulation of ribosomal genes had been widely accepted, mostly based on transcriptomic studies in yeast 56,57 .”
From Single-cell transcriptomic profiling of the aging mouse brain (2019) · cites this paper as [56]
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Citations
Cites
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
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Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Self-citationSpatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
Self-citationRELATIONS BETWEEN TWO SETS OF VARIATES
Hotelling, H. (1936) · Biometrika · doi:10.1093/biomet/28.3-4.321 · Cited by 6,041
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944
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Picelli, S., Faridani, O. R., Björklund, Å. K., Winberg, G., Sagasser, S., & Sandberg, R. (2014) · Nature Protocols · doi:10.1038/nprot.2014.006 · Cited by 4,137
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Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Self-citationIntegrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508
Self-citationFast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488
Dimensionality reduction for visualizing single-cell data using UMAP
Becht, E., McInnes, L., Healy, J., Dutertre, C.-A., Kwok, I. W. H., Ng, L. G., Ginhoux, F., & Newell, E. W. (2019) · Nature Biotechnology · doi:10.1038/nbt.4314 · Cited by 5,358
Hafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479
Self-citation
Metadata
- Authors
- Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R.
- Affiliations
- New York Genome Center (US)
- Corresponding author
- Satija, Rahul
- Venue
- Nature Biotechnology
- Published
- 2018-04-02
- Volume
- 36
- Issue
- 5
- Pages
- 411-420
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC6700744
- OpenAlex
- W2794480084
- Cited by
- 13,174
- References
- 78
- Citations by year
- 2026: 1297 · 2025: 1838 · 2024: 2118 · 2023: 1918 · 2022: 1783 · 2021: 1824 · 2020: 1416 · 2019: 760 · 2018: 205
- FWCI
- 381.75
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (1342536)
- National Institutes of Health (1dp2hg009623-01)
- National Human Genome Research Institute (DP2-HG-009623)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- Seurat, peripheral blood mononuclear cells, hematopoietic progenitor cells, pancreatic islets, cell state transitions, statistical power
- MeSH terms
- Data Analysis, Animals, Humans, Leukocytes, Mononuclear, Software, Sequence Analysis, RNA, Gene Expression Profiling, Computers, Molecular, Mice, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome
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