Comprehensive Integration of Single-Cell Data
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Abstract
Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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When cell-type-specific batch effects are present, or there is a shift in the percentage of cell types across experiments, non-linear batch effect correction strategies are needed.
“but note that when cell-type-specific batch effects are present, or there is a shift in the percentage of cell types across experiments, non-linear batch effect correction strategies are needed [18].”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [18]
In Results, “Constructing integrated atlases at the scale of organs and organisms” (page unavailable), the paper describes a non-linear, locally weighted correction and reports that it remains robust when cell types are absent from some datasets. It does not say that non-linear strategies are necessary when batch effects differ by cell type or cell-type percentages shift across experiments. (LLM, conf=0.85)
Experimental — accuracy not yet measuredSamples were evaluated for potential batch effects and integrated using the Seurat Integration workflow with SCTransform normalization.
“After merging, the samples were evaluated for potential batch effects.”
From cGAS–STING drives ageing-related inflammation and neurodegeneration (2023) · cites this paper as [60]
The Results section describes evaluating dataset mixing after Seurat v3 integration, and STAR Methods describes its batch-correction workflow. But the paper does not establish that the citing authors evaluated their samples after merging; STAR Methods instead specifies log-normalization, not SCTransform normalization. No page numbers are available in our copy. (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 [78]
The STAR Methods (Method details; page unavailable) identify FindIntegrationAnchors as an anchor-identification function and IntegrateData as part of the integration procedure used for batch correction. But the paper describes CCA, not reciprocal PCA, as the initial dimension-reduction method for reference integration. It therefore supports the Seurat functions and batch-correction approach, but not the specified reciprocal-PCA workflow. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe 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 [6]
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 [6]
The most widely used analysis packages in the field currently employ DE methods prone to false discoveries by default.
“the most widely used analysis packages in the field currently employ DE methods prone to false discoveries by default 35 , 36 .”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [35]
The STAR Methods (Quantification and statistical analysis; page unavailable) describe differential-expression testing with Seurat’s FindMarkers, but specify a test rather than identifying its default. Our copy does not assess whether widely used packages’ default DE methods are prone to false discoveries (searched: false discoveries, false positives, and default DE tests). (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe quality of cDNA and library was examined by a Bioanalyzer at each step.
“At each step, the quality of cDNA and library was examined by a Bioanalyzer [48, 49].”
From Single-cell transcriptomics unveils skin cell specific antifungal immune responses and IL-1Ra- IL-1R immune evasion strategies of emerging fungal pathogen Candida auris (2024) · cites this paper as [49]
The STAR Methods section mentions cDNA amplification and RNA-seq library preparation but does not say their quality was checked with a Bioanalyzer at each step. Searches for “Bioanalyzer” and cDNA/library quality control found no passage addressing that procedure. (LLM, conf=0.73)
Experimental — accuracy not yet measuredThe Guo et al. pipeline uses Seurat and CLUE to identify 281 FDA-approved drugs potentially effective for treating COVID-19.
“Another pipeline by Guo et al. uses a simple combination of Seurat18, a tool for scRNA-seq analysis, and CLUE to identify 281 FDA-approved drugs that can potentially be effective for treating COVID-1919.”
From ASGARD is a single-cell guided pipeline to aid repurposing of drugs (2023) · cites this paper as [18]
Seurat, DESeq2, edgeR, and Limma are among the top methods for discovering differentially expressed genes using single-cell data.
“Previous benchmark study showed that Seurat18, DESeq226, edgeR27, and Limma25 are among the top methods in discovering differentially expressed genes using single-cell data28.”
From ASGARD is a single-cell guided pipeline to aid repurposing of drugs (2023) · cites this paper as [18]
A 38,606-cell wild-type reference atlas was generated from dissociated WT embryos at 6, 8 and 10 hpf (2 technical replicates per stage) and used Seurat's label transfer function to cluster and label WT reference cells according to Farrell et al. annotations.
“First, we generated a 38,606-cell wild-type (WT) reference atlas from dissociated WT embryos at 6, 8 and 10 hpf (2 technical replicates per stage) and used Seurat’s label transfer function39 to cluster and label the WT reference cells according to the annotations in Farrell et al.32 (Extended Data Fig. 11).”
From Dissecting cell identity via network inference and in silico gene perturbation (2023) · cites this paper as [39]
SCING GRN module based dimensionality reduction corrects for batch effect and retains biological information in a similar manner to dedicated batch correction methods such as FastMNN, Harmony, and Seurat.
“We found that the SCING GRN module based dimensionality reduction carried the ability to correct for batch effect and retain biological information (Figure S6) in a similar manner to dedicated batch correction methods such as FastMNN,41 Harmony,42 and Seurat.43”
From SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics (2023) · cites this paper as [43]
Sequencing depth per cell, number of unique features, and mitochondrial gene percentage were regressed out.
“The resulting normalized counts were then transformed from log2 scale to the natural log scale for compatibility with Seurat v355 and the effects of sequencing depth per cell, number of unique features, and percentage of mitochondrial genes were regressed out.”
From Meningeal lymphatics affect microglia responses and anti-Aβ immunotherapy (2021) · cites this paper as [55]
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 [3]
Unsupervised clustering identified all of the major cell types of the mouse spinal cord.
“Unsupervised clustering30 identified all of the major cell types of the mouse spinal cord (Extended Data Fig. 3h–m).”
From The neurons that restore walking after paralysis (2022) · cites this paper as [30]
Prior to clustering analysis, batch effect correction and data integration were performed across the two different experimental conditions.
“Prior to clustering analysis, we first performed batch effect correction and data integration across the two different experimental conditions as previously described30.”
From The neurons that restore walking after paralysis (2022) · cites this paper as [30]
Using Seurat integration, cells and nuclei from all six studies were spatially interposed in a UMAP visualization of principal component space and separated into groupings that each expressed a panel of well-established cell type markers.
“With this approach, the cells and nuclei from all six studies were spatially interposed in a UMAP visualization of principal component space (Fig. 1c) and separated into groupings that each expressed a panel of well-established cell type markers such as Snap25 (neurons), Mbp (oligodendrocytes), Aqp4 (astrocytes), and Ctss (microglia) (Fig. 1d, e).”
From A harmonized atlas of mouse spinal cord cell types and their spatial organization (2021) · cites this paper as [23]
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- Authors
- Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R.
- Affiliations
- New York Genome Center (US)
- Corresponding author
- Satija, Rahul
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2019-06
- Volume
- 177
- Issue
- 7
- Pages
- 1888-1902.e21
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6687398
- OpenAlex
- W2949177718
- Cited by
- 15,453
- References
- 100
- Citations by year
- 2026: 1890 · 2025: 2347 · 2024: 2604 · 2023: 2455 · 2022: 2564 · 2021: 2244 · 2020: 1160 · 2019: 166 · 2018: 6 · 2014: 1 · 2013: 1
- FWCI
- 515.23
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (1DP2HG009623- 01, 1OT2OD026673-01, 5R01MH071679-12)
- Chan Zuckerberg Awards (HCA-A-1704-01895, HCA2-A-1708-02755)
- NSF (DGE1342536)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single-cell data integration, single-cell RNA sequencing, single-cell ATAC-seq, multi-omics integration, chromatin accessibility, interneurons, lymphocyte populations, in situ gene expression, spatial transcriptomics, cell identity
- MeSH terms
- Humans, Software, Sequence Analysis, RNA, Gene Expression Profiling, Databases, Nucleic Acid, Single-Cell Analysis, Transcriptome
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