Integrating single-cell transcriptomic data across different conditions, technologies, and species
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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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Five methods commonly in use for low-MOI single-cell CRISPR screen association testing are Seurat-Wilcox, MIMOSCA, t-test, MAST, and KS test.
“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]
The Online Methods section, under “Integrated Analysis Across Datasets” (page unavailable), reports using a Wilcoxon rank-sum test for differential-expression testing in Seurat. It does not identify FindMarkers() or establish that Wilcoxon is that function’s default. The citation [19] is attached to the Seurat method, not to the other four methods in the citing sentence. (LLM, conf=0.82)
Experimental — accuracy not yet measuredBatch effects were corrected using FindIntegrationAnchor() and IntegrateData() in the reciprocal PCA approach of the Seurat workflow.
“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]
The Results section (‘Overview of Seurat alignment workflow’; page unavailable) supports the general point that Seurat integrates datasets affected by batch differences. It describes a CCA-based alignment workflow, however, not reciprocal PCA, and the retrieved passages do not identify FindIntegrationAnchor() or IntegrateData() as its functions. (LLM, conf=0.88)
Experimental — accuracy not yet measuredTumor KCs broadly recapitulated the normal basal, cycling, and differentiating subpopulations, with several shared marker genes across normal skin and cSCC.
“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 hdWGCNA R package extends the data structures and functionality of the Seurat package.
“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]
hdWGCNA was developed as an R package extending Seurat and has an extensive documentation website demonstrating its use on new datasets.
“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]
Upregulation of CEBPB and CEBPD suggested a differentiation state into the granulocytic lineage.
“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]
The Results section discusses hematopoietic progenitor lineages, but does not link CEBPB or CEBPD upregulation to granulocytic differentiation. Searches for those genes, emergency granulopoiesis, and granulopoiesis under cellular stress found no passage addressing the claim; page numbers were unavailable in our copy. (LLM, conf=0.72)
Experimental — accuracy not yet measuredAll single-cell samples were processed simultaneously in the same experiment, and sample origin was not regressed out in the Seurat analysis.
“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
The keratinocyte feature deviation matrix was processed to remove plate and experiment batch effects.
“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
All samples were processed together in one experiment, so sample origin was not regressed out.
“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
Significant diversity and functional heterogeneity exist within the fibroblast population during organ fibrosis.
“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]
Many unsupervised scRNA-seq clustering methods have been proposed, which are followed by time-consuming and labor-costly annotations.
“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]
Widely used alignment methods treat dataset differences as technical and remove them, or project datasets into a shared latent space using maximally correlated dimensions.
“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
For the PBMC and pancreas datasets, the authors reproduced the analyses as described by Butler et al. and later Seurat tutorials.
“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
To remove any potential batch effect between saline and cocaine samples, CCA analysis was run.
“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]
Canonical correlation analysis (CCA) was used to find a projection space that maximizes the correlation between the two samples so that batch effect can be removed.
“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]
Popular platforms such as Seurat, Scater, or Scanpy limit themselves to tools developed in their respective programming languages.
“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
There is currently no consensus on whether or not to perform normalization over genes.
“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
The popular Seurat tutorials generally apply gene scaling.
“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
Data integration methods such as CCA, MNN, Scanorama, RISC, scGen, LIGER, BBKNN, and Harmony have been developed to overcome compositional differences.
“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
Currently available platforms exist on the command line in R or Python, and as local applications or Web servers with graphical user interfaces (GUIs).
“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
Among command line platforms, Scater and Seurat easily interface with the large variety of analysis tools available via the R Bioconductor project.
“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
Aging-driven down-regulation of ribosomal genes had been widely accepted, mostly based on transcriptomic studies in yeast.
“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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Clonal replacement of tumor-specific T cells following PD-1 blockade
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Eleven grand challenges in single-cell data science
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BackgroundMethodInfluentialSpatiotemporal Immune Landscape of Colorectal Cancer Liver Metastasis at Single-Cell Level
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“Linear-embedding models such as canonical correlation analysis 36 and Harmony 37 were shown to perform well for batch correction on simpler integration tasks with distinct batch structures 38,39 . scANVI 40 can incorporate the cell-type labels, which is favourable as it can help to conserve…”
MethodSquidpy: a scalable framework for spatial omics analysis
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The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans
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- 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 · 2017: 5 · 2015: 1 · 2014: 1 · 2013: 1
- 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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