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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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“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
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“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]
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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]
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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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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
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update
Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S. L., Jagodnik, K. M., Lachmann, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw377 · Cited by 10,799
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
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
Full-length RNA-seq from single cells using Smart-seq2
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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Research works printed but not linked: 2
- Zhou, F. & Torre, F. in Advances in Neural Information Processing Systems 22; NIPS 2009 (eds. Y. Bengio, Y., Schuurmans, D., Lafferty, J.D., Williams, C.K.I. & Culotta, A.) https://papers.nips.cc/paper/3728-canonical-time-warping-for-alignment-of-human-behavior (Neural Information Processing Systems Foundation, Inc., 2009).
- Wang, C. & Mahadevan, S. in Proc. Twenty-Second International Joint Conference on Artificial Intelligence, Vol. 2 (ed. Walsh, T.) 1541–1546 (AAAI, 2011).
Cited by
Sorted by citations
Items 701–800 of 1,197
Schreurs, R., Baumdick, M. E., Sagebiel, A. F., Kaufmann, M., Mokrý, M., Klarenbeek, P. L., Schaltenberg, N., Steinert, F. L., van Rijn, J. M., Drewniak, A., et al. (2019) · Immunity · doi:10.1016/j.immuni.2018.12.010 · Cited by 206
Spencer, S., Bal, S. K., Egner, W., Allen, H. L., Raza, S. I., Ma, C. A., Gürel, M., Zhang, Y., Sun, G., Sabroe, R. A., et al. (2019) · The Journal of Experimental Medicine · doi:10.1084/jem.20190344 · Cited by 208
Guo, C., Li, B., Ma, H., Wang, X., Cai, P., Yu, Q., Zhu, L., Jin, L., Jiang, C., Fang, J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17834-w · Cited by 203
Xing, X., Yang, F., Huang, Q., Guo, H., Li, J., Qiu, M., Bai, F., & Wang, J. (2021) · Science Advances · doi:10.1126/sciadv.abd9738 · Cited by 204
Chromatin accessibility profiling methods
Minnoye, L., Marinov, G. K., Krausgruber, T., Pan, L., Marand, A. P., Secchia, S., Greenleaf, W. J., Furlong, E. E. M., Zhao, K., Schmitz, R. J., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-020-00008-9 · Cited by 206
In vitro culture of cynomolgus monkey embryos beyond early gastrulation
Ma, H., Zhai, J., Wan, H., Jiang, X., Wang, X., Wang, L., Xiang, Y., He, X., Zhao, Z., Zhao, B., et al. (2019) · Science · doi:10.1126/science.aax7890 · Cited by 203
Diversity of peripheral blood human NK cells identified by single-cell RNA sequencing
Smith, S. L., Kennedy, P. R., Stacey, K., Worboys, J. D., Yarwood, A., Seo, S., Hegewisch-Solloa, E., Mistretta, B., Chatterjee, S. S., Gunaratne, P. H., et al. (2020) · Blood Advances · doi:10.1182/bloodadvances.2019000699 · Cited by 209
Genetically engineered myeloid cells rebalance the core immune suppression program in metastasis
Kaczanowska, S., Beury, D. W., Gopalan, V., Tycko, A. K., Qin, H., Clements, M. E., Drake, J. A., Nwanze, C., Murgai, M., Rae, Z., et al. (2021) · Cell · doi:10.1016/j.cell.2021.02.048 · Cited by 208
Zhang, S., Fang, W., Zhou, S., Zhu, D., Chen, R., Gao, X., Li, Z., Fu, Y. S., Zhang, Y., Yang, F., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40727-7 · Cited by 214
Spatially organized cellular communities form the developing human heart
Farah, E. N., Hu, R., Kern, C., Zhang, Q., Lu, T.-Y., Ma, Q., Tran, S., Zhang, B., Carlin, D. E., Monell, A., et al. (2024) · Nature · doi:10.1038/s41586-024-07171-z · Cited by 213
Zhu, Z., Achreja, A., Meurs, N., Animasahun, O., Owen, S., Mittal, A., Parikh, P., Lo, T.-W., Franco-Barraza, J., Shi, J., et al. (2020) · Nature Metabolism · doi:10.1038/s42255-020-0226-5 · Cited by 204
Single-cell analysis of prenatal and postnatal human cortical development
Velmeshev, D., Perez, Y., Yan, Z., Valencia, J. E., Castaneda-Castellanos, D. R., Wang, L., Schirmer, L., Mayer, S., Wick, B., Wang, S., et al. (2023) · Science · doi:10.1126/science.adf0834 · Cited by 204
Single-cell analysis of germinal-center B cells informs on lymphoma cell of origin and outcome
Holmes, A. B., Corinaldesi, C., Shen, Q. “., Kumar, R., Compagno, N., Wang, Z., Nitzan, M., Grunstein, E., Pasqualucci, L., Dalla‐Favera, R., et al. (2020) · The Journal of Experimental Medicine · doi:10.1084/jem.20200483 · Cited by 209
Reprogramming roadmap reveals route to human induced trophoblast stem cells
Liu, X., Ouyang, J. F., Rossello, F. J., Tan, J. P., Davidson, K. C., Valdes, D. S., Schröder, J., Sun, Y., Chen, J., Knaupp, A. S., et al. (2020) · Nature · doi:10.1038/s41586-020-2734-6 · Cited by 202
Intravenous nanoparticle vaccination generates stem-like TCF1+ neoantigen-specific CD8+ T cells
Baharom, F., Ramirez-Valdez, R. A., Tobin, K. K. S., Yamane, H., Dutertre, C., Khalilnezhad, A., Reynoso, G. V., Coble, V. L., Lynn, G. M., Mulè, M. P., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-00810-3 · Cited by 205
Single-Cell Analysis of Blood-Brain Barrier Response to Pericyte Loss
Mäe, M. A., He, L., Nordling, S., Vázquez-Liébanas, E., Nahar, K., Jung, B., Li, X., Tan, B. C., Foo, J. C., Cazenave‐Gassiot, A., et al. (2020) · Circulation Research · doi:10.1161/circresaha.120.317473 · Cited by 220
Dissecting the treatment-naive ecosystem of human melanoma brain metastasis
Biermann, J., Melms, J. C., Amin, A. D., Wang, Y., Caprio, L., Karz, A., Tagore, S., Barrera, I., Ibarra-Arellano, M. A., Andreatta, M., et al. (2022) · Cell · doi:10.1016/j.cell.2022.06.007 · Cited by 207
Autophagy enables microglia to engage amyloid plaques and prevents microglial senescence
Choi, I., Wang, M., Yoo, S., Xu, P., Seegobin, S. P., Li, X., Han, X., Wang, Q., Peng, J., Zhang, B., et al. (2023) · Nature Cell Biology · doi:10.1038/s41556-023-01158-0 · Cited by 202
Kim, D., Kobayashi, T., Voisin, B., Jo, J., Sakamoto, K., Jin, S., Kelly, M. C., Pasieka, H. B., Naff, J. L., Meyerle, J. H., et al. (2020) · Nature Medicine · doi:10.1038/s41591-019-0733-7 · Cited by 206
Hu, Z., Artibani, M., Alsaadi, A., Wietek, N. C., Morotti, M., Shi, T., Zhong, Z., González, L. S., El-Sahhar, S., Carrami, E. M., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.01.003 · Cited by 195
Kim, S. S., Shen, S. A., Miyauchi, S., Sanders, P. D., Pietryga, I. F., Mell, L. K., Gutkind, J. S., Cohen, E. E. W., Califano, J. A., & Sharabi, A. B. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-3211 · Cited by 200
Chen, J., Li, K., Chen, J., Wang, X., Ling, R., Cheng, M., Chen, Z., Chen, F., He, Q., Li, S., et al. (2022) · 癌症：英文版 · doi:10.1002/cac2.12273 · Cited by 202
Hu, Q., Hong, Y., Qi, P., Lu, G., Mai, X., Xu, S., He, X., Guo, Y., Gao, L., Jing, Z., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22300-2 · Cited by 201
A single-cell transcriptomic landscape of the lungs of patients with COVID-19
Wang, S., Yao, X., Ma, S., Ping, Y., Fan, Y., Sun, S., He, Z., Shi, Y., Sun, L., Xiao, S., et al. (2021) · Nature Cell Biology · doi:10.1038/s41556-021-00796-6 · Cited by 195
Augsornworawat, P., Maxwell, K. G., Velazco-Cruz, L., & Millman, J. R. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108067 · Cited by 200
Long-Term Expansion of Pancreatic Islet Organoids from Resident Procr+ Progenitors
Wang, D., Wang, J., Bai, L., Pan, H., Feng, H., Clevers, H. C., & Zeng, Y. A. (2020) · Cell · doi:10.1016/j.cell.2020.02.048 · Cited by 202
Combined PD-1, BRAF and MEK inhibition in BRAFV600E colorectal cancer: a phase 2 trial
Tian, J., Chen, J. H., Chao, S. X., Pelka, K., Giannakis, M., Hess, J. M., Burke, K. P., Jorgji, V., Sindurakar, P., Braverman, J., et al. (2023) · Nature Medicine · doi:10.1038/s41591-022-02181-8 · Cited by 205
Bernink, J. H., Ohne, Y., Teunissen, M. B. M., Wang, J., Wu, J., Krabbendam, L., Guntermann, C., Volckmann, R., Köster, J., van Tol, S., et al. (2019) · Nature Immunology · doi:10.1038/s41590-019-0423-0 · Cited by 202
Conway, B. R., O'Sullivan, E. D., Cairns, C., O’Sullivan, J., Simpson, D. J., Salzano, A., Connor, K. L., Ding, P., Humphries, D. C., Stewart, K., et al. (2020) · Journal of the American Society of Nephrology · doi:10.1681/asn.2020060806 · Cited by 201
Thomas, J. R., Appios, A., Zhao, X., Dutkiewicz, R., Donde, M. J., Lee, C. Y. C., Naidu, P., Lee, C., Cerveira, J., Liu, B., et al. (2020) · The Journal of Experimental Medicine · doi:10.1084/jem.20200891 · Cited by 192
Lopes, N., McIntyre, C., Martin, S., Raverdeau, M., Sumaria, N., Kohlgruber, A. C., Fiala, G. J., Agudelo, L. Z., Dyck, L., Kane, H., et al. (2021) · Nature Immunology · doi:10.1038/s41590-020-00848-3 · Cited by 202
Bangert, C., Rindler, K., Krausgruber, T., Alkon, N., Thaler, F. M., Kurz, H., Ayub, T., Demirtas, D., Fortelny, N., Vorstandlechner, V., et al. (2021) · Science Immunology · doi:10.1126/sciimmunol.abe2749 · Cited by 205
A single-nuclei RNA sequencing study of Mendelian and sporadic AD in the human brain
Del-Aguila, J. L., Li, Z., Dube, U., Mihindukulasuriya, K. A., Budde, J. P., Fernandez, M. V., Ibanez, L., Bradley, J., Wang, F., Bergmann, K., et al. (2019) · Alzheimer's Research & Therapy · doi:10.1186/s13195-019-0524-x · Cited by 183
Single-Cell Transcriptomics: A High-Resolution Avenue for Plant Functional Genomics
Rich‐Griffin, C., Stechemesser, A., Finch, J., Lucas, E. S., Ott, S., & Schäfer, P. (2019) · Trends in Plant Science · doi:10.1016/j.tplants.2019.10.008 · Cited by 199
Enteric Nervous System-Derived IL-18 Orchestrates Mucosal Barrier Immunity
Jarret, A. P., Jackson, R. P., Duizer, C., Healy, M. E., Zhao, J., Rone, J. M., Bielecki, P., Sefik, E., Roulis, M., Rice, T., et al. (2020) · Cell · doi:10.1016/j.cell.2019.12.016 · Cited by 211
Unblending of Transcriptional Condensates in Human Repeat Expansion Disease
Basu, S., Mackowiak, S. D., Niskanen, H., Knežević, D., Asimi, V., Grosswendt, S., Geertsema, H. J., Ali, S., Jerković, I., Ewers, H., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.018 · Cited by 193
Yang, Y., Sun, H., Zhang, Y., Zhang, T., Gong, J., Wei, Y., Duan, Y., Shu, M., Yang, Y., Wu, D., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109442 · Cited by 203
Normalizing and denoising protein expression data from droplet-based single cell profiling
Mulè, M. P., Martins, A. J., & Tsang, J. S. (2022) · Nature Communications · doi:10.1038/s41467-022-29356-8 · Cited by 197
Sex differences in neutrophil biology modulate response to type I interferons and immunometabolism
Gupta, S., Nakabo, S., Blanco, L. P., O’Neil, L. J., Wigerblad, G., Goel, R. R., Mistry, P., Jiang, K., Carmona‐Rivera, C., Chan, D. W., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2003603117 · Cited by 196
Pedroza, A. J., Tashima, Y., Shad, R., Cheng, P. P., Wirka, R. C., Churovich, S., Nakamura, K., Yokoyama, N., Cui, J. Z., Iosef, C., et al. (2020) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.120.314670 · Cited by 197
Genetic mapping of etiologic brain cell types for obesity
Timshel, P. N., Thompson, J. J., & Pers, T. H. (2020) · eLife · doi:10.7554/elife.55851 · Cited by 202
Claes, C., Danhash, E. P., Hasselmann, J., Chadarevian, J. P., Shabestari, S. K., England, W. E., Lim, T. E., Hidalgo, J. L. S., Spitale, R. C., Davtyan, H., et al. (2021) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00473-0 · Cited by 197
Super-resolved spatial transcriptomics by deep data fusion
Bergenstråhle, L., He, B., Bergenstråhle, J., Abalo, X., Mirzazadeh, R., Thrane, K., Ji, A. L., Andersson, A., Larsson, L., Stakenborg, N., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01075-3 · Cited by 188
The Myeloid Cell Compartment—Cell by Cell
Bassler, K. E., Schulte-Schrepping, J., Warnat‐Herresthal, S., Aschenbrenner, A. C., & Schultze, J. L. (2019) · Annual Review of Immunology · doi:10.1146/annurev-immunol-042718-041728 · Cited by 200
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Fishell, G., & Kepecs, Á. (2019) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-070918-050421 · Cited by 212
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Gambardella, G., Viscido, G., Tumaini, B., Isacchi, A., Bosotti, R., & di Bernardo, D. (2022) · Nature Communications · doi:10.1038/s41467-022-29358-6 · Cited by 204
McFarland, J. M., Paolella, B. R., Warren, A., Geiger-Schuller, K., Shibue, T., Rothberg, M., Kuksenko, O., Colgan, W. N., Jones, A., Chambers, E., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17440-w · Cited by 173
Xu, R., Li, X., Boreland, A. J., Posyton, A., Kwan, K. Y., Hart, R. P., & Jiang, P. (2020) · Nature Communications · doi:10.1038/s41467-020-15411-9 · Cited by 200
Rudman-Melnick, V., Adam, M., Potter, A., Chokshi, S. M., Ma, Q., Drake, K. A., Schuh, M. P., Kofron, M. J., Devarajan, P., & Potter, S. S. (2020) · Journal of the American Society of Nephrology · doi:10.1681/asn.2020010052 · Cited by 193
Human and mouse trigeminal ganglia cell atlas implicates multiple cell types in migraine
Yang, L., Xu, M., Bhuiyan, S. A., Li, J., Zhao, J., Cohrs, R. J., Susterich, J. T., Signorelli, S., Green, U., Stone, J. R., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.03.003 · Cited by 192
Zhang, C., Li, J., Cheng, Y., Meng, F., Song, J., Fan, X., Fan, H., Li, J., Fu, Y., Zhou, M., et al. (2022) · Gut · doi:10.1136/gutjnl-2021-325915 · Cited by 202
Extensive Remodeling of the Immune Microenvironment in B Cell Acute Lymphoblastic Leukemia
Witkowski, M. T., Dolgalev, I., Evensen, N. A., Ma, C., Chambers, T. M., Roberts, K. G., Sreeram, S., Dai, Y., Tikhonova, A. N., Lasry, A., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.04.015 · Cited by 200
Single nuclei RNA-seq of mouse placental labyrinth development
Marsh, B., & Blelloch, R. H. (2020) · eLife · doi:10.7554/elife.60266 · Cited by 195
Takeda, A., Hollmén, M., Đermadi, D., Pan, J., Brulois, K., Kaukonen, R., Lönnberg, T., Boström, P., Koskivuo, I., Irjala, H., et al. (2019) · Immunity · doi:10.1016/j.immuni.2019.06.027 · Cited by 183
Winkler, E. S., Shrihari, S., Hykes, B. L., Handley, S. A., Andhey, P. S., Huang, Y. S., Swain, A., Droit, L., Chebrolu, K. K., Mack, M., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.029 · Cited by 190
Walgrave, H., Balusu, S., Snoeck, S., Vanden Eynden, E., Craessaerts, K., Thrupp, N., Wolfs, L., Horré, K., Fourne, Y., Ronisz, A., et al. (2021) · Cell Stem Cell · doi:10.1016/j.stem.2021.05.001 · Cited by 175
Olbrecht, S., Busschaert, P., Qian, J., Vanderstichele, A., Loverix, L., Gorp, T. V., Nieuwenhuysen, E. V., Han, S., den Broeck, A. V., Coosemans, A., et al. (2021) · Genome Medicine · doi:10.1186/s13073-021-00922-x · Cited by 195
Shared mechanisms across the major psychiatric and neurodegenerative diseases
Wingo, T. S., Liu, Y., Gerasimov, E. S., Vattathil, S. M., Wynne, M. E., Liu, J., Lori, A., Faundez, V., Bennett, D. A., Seyfried, N. T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31873-5 · Cited by 190
Evolution of immune and stromal cell states and ecotypes during gastric adenocarcinoma progression
Wang, R., Song, S., Qin, J., Yoshimura, K., Peng, F., Chu, Y., Li, Y., Fan, Y., Jin, J., Dang, M., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.06.005 · Cited by 198
Temporal modelling using single-cell transcriptomics
Ding, J., Sharon, N., & Bar-Joseph, Z. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00444-7 · Cited by 195
Kotliarov, Y., Sparks, R., Martins, A. J., Mulè, M. P., Lu, Y. C., Goswami, M., Kardava, L., Banchereau, R., Pascual, V., Biancotto, A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0769-8 · Cited by 184
Mouse and human share conserved transcriptional programs for interneuron development
Shi, Y., Wang, M., Mi, D., Lu, T., Wang, B., Dong, H., Zhong, S., Chen, Y., Sun, L., Zhou, X., et al. (2021) · Science · doi:10.1126/science.abj6641 · Cited by 196
Decoding molecular and cellular heterogeneity of mouse nucleus accumbens
Chen, R., Blosser, T. R., Djekidel, M. N., Hao, J., Bhattacherjee, A., Chen, W., Tuesta, L. M., Zhuang, X., & Zhang, Y. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00938-x · Cited by 191
Duraiswamy, J., Turrini, R., Minasyan, A., Barras, D., Crespo, I., Grimm, A. J., Casado, J., Genolet, R., Benedetti, F., Wicky, A. M., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.10.008 · Cited by 191
Nixon, B. G., Kuo, F., Ji, L., Liu, M., Capistrano, K. J., Do, M., Franklin, R. A., Wu, X., Kansler, E. R., Srivastava, R. M., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.10.002 · Cited by 195
Li, R., Bernau, K., Sandbo, N. K., Gu, J., Preißl, S., & Sun, X. (2018) · eLife · doi:10.7554/elife.36865 · Cited by 190
Human Primordial Germ Cells Are Specified from Lineage-Primed Progenitors
Chen, D., Sun, N., Hou, L., Kim, R., Faith, J. E., Aslanyan, M. M., Tao, Y., Zheng, Y., Fu, J., Liu, W., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.11.083 · Cited by 184
Histone H3.3G34-Mutant Interneuron Progenitors Co-opt PDGFRA for Gliomagenesis
Chen, C. C. L., Deshmukh, S., Jessa, S., Hadjadj, D., Lisi, V., Andrade, A. F., Faury, D., Jawhar, W., Dali, R., Suzuki, H., et al. (2020) · Cell · doi:10.1016/j.cell.2020.11.012 · Cited by 199
Wang, K., Wang, S., Chen, Y., Wu, D., Hu, X., Lu, Y., Wang, L., Bao, L., Li, C., & Zhang, X. (2021) · Cell Research · doi:10.1038/s41422-021-00479-9 · Cited by 196
Spatially resolved isotope tracing reveals tissue metabolic activity
Wang, L., Xing, X., Zeng, X., Jackson, S. R., TeSlaa, T. A., Al‐Dalahmah, O., Samarah, L. Z., Goodwin, K., Yang, L., McReynolds, M. R., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01378-y · Cited by 191
Bagley, S. J., Binder, Z. A., Lamrani, L., Marinari, E., Desai, A. S., Nasrallah, M. P., Maloney, E., Brem, S. S., Lustig, R. A., Kurtz, G. A., et al. (2024) · Nature Cancer · doi:10.1038/s43018-023-00709-6 · Cited by 199
Zalocusky, K. A., Najm, R., Taubes, A. L., Hao, Y., Yoon, S. Y., Koutsodendris, N., Nelson, M. R., Rao, A., Bennett, D. A., Bant, J., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00851-3 · Cited by 189
Transformer for one stop interpretable cell type annotation
Chen, J., Xu, H., Tao, W., Chen, Z., Zhao, Y., & Han, J.-D. J. (2023) · Nature Communications · doi:10.1038/s41467-023-35923-4 · Cited by 189
Nuclei multiplexing with barcoded antibodies for single-nucleus genomics
Gaublomme, J. T., Li, B., Aubin, C., Knecht, A., Yang, Y., Drokhlyansky, E., Wittenberghe, N. V., Waldman, J., Dionne, D. A., Nguyễn, L., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10756-2 · Cited by 186
A repeated molecular architecture across thalamic pathways
Phillips, J. W., Schulmann, A., Hara, E., Winnubst, J., Liu, C., Valakh, V., Wang, L., Shields, B. C., Korff, W. L., Chandrashekar, J., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0483-3 · Cited by 192
Cheung, P., Xiol, J., Dill, M. T., Yuan, W.-C., Panero, R., Roper, J., Osorio, F. G., Maglic, D., Li, Q., Gurung, B., et al. (2020) · Cell stem cell · doi:10.1016/j.stem.2020.07.003 · Cited by 191
Contribution of plasma cells and B cells to hidradenitis suppurativa pathogenesis
Gudjonsson, J. E., Tsoi, L. C., Ma, F., Billi, A. C., van Straalen, K. R., Vossen, A. R. J. V., van der Zee, H. H., Harms, P. W., Wasikowski, R., Yee, C. M., et al. (2020) · JCI Insight · doi:10.1172/jci.insight.139930 · Cited by 188
Oxidative Metabolism Drives Immortalization of Neural Stem Cells during Tumorigenesis
Bonnay, F., Veloso, A., Steinmann, V., Köcher, T., Abdusselamoglu, M. D., Bajaj, S., Rivelles, E., Landskron, L., Esterbauer, H., Zinzen, R. P., et al. (2020) · Cell · doi:10.1016/j.cell.2020.07.039 · Cited by 191
Kernfeld, E. M., Genga, R. M. J., Neherin, K., Magaletta, M. E., Xu, P., & Maehr, R. (2018) · Immunity · doi:10.1016/j.immuni.2018.04.015 · Cited by 188
Dermal adipose tissue has high plasticity and undergoes reversible dedifferentiation in mice
Zhang, Z., Shao, M., Hepler, C., Zi, Z., Zhao, S., An, Y., Zhu, Y., Ghaben, A. L., Wang, M.-Y., Li, N., et al. (2019) · Journal of Clinical Investigation · doi:10.1172/jci130239 · Cited by 191
GCN2 drives macrophage and MDSC function and immunosuppression in the tumor microenvironment
Halaby, M. J., Hezaveh, K., Lamorte, S., Ciudad, M. T., Kloetgen, A., MacLeod, B. L., Guo, M., Chakravarthy, A., Medina, T. D. S., Ugel, S., et al. (2019) · Science Immunology · doi:10.1126/sciimmunol.aax8189 · Cited by 190
Efficient Strategies for Microglia Replacement in the Central Nervous System
Xu, Z., Rao, Y., Huang, Y., Zhou, T. G., Feng, R., Xiong, S., Yuan, T., Qin, S., Lu, Y., Zhou, X., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108041 · Cited by 189
Ferreira, R. M., Sabo, A. R., Winfree, S., Collins, K. S., Janosevic, D., Gulbronson, C. J., Cheng, Y., Casbon, L., Barwinska, D., Ferkowicz, M. J., et al. (2021) · JCI Insight · doi:10.1172/jci.insight.147703 · Cited by 187
The regulatory landscape of Arabidopsis thaliana roots at single-cell resolution
Dorrity, M. W., Alexandre, C. M., Hamm, M. O., Vigil, A.-L. B. G., Fields, S., Queitsch, C., & Cuperus, J. T. (2021) · Nature Communications · doi:10.1038/s41467-021-23675-y · Cited by 196
Agarwal, S., Aznar, M. A., Rech, A. J., Good, C. R., Kuramitsu, S., Da, T., Gohil, M., Chen, L., Hong, S.-J. A., Ravikumar, P., et al. (2023) · Immunity · doi:10.1016/j.immuni.2023.09.001 · Cited by 199
An atlas of epithelial cell states and plasticity in lung adenocarcinoma
Han, G., Sinjab, A. F., Rahal, Z., Lynch, A. M., Treekitkarnmongkol, W., Liu, Y., Serrano, A. G., Feng, J., Liang, K., Khan, K. B., et al. (2024) · Nature · doi:10.1038/s41586-024-07113-9 · Cited by 185
Neuronal defects in a human cellular model of 22q11.2 deletion syndrome
Khan, T. A., Revah, O., Gordon, A., Yoon, S.-J., Krawisz, A. K., Goold, C., Sun, Y., Kim, C. H., Tian, Y., Li, M.-Y., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1043-9 · Cited by 186
Pooled Knockin Targeting for Genome Engineering of Cellular Immunotherapies
Roth, T. L., Li, P. J., Blaeschke, F., Nies, J. F., Apathy, R., Mowery, C., Yu, R., Nguyen, M. L. T., Lee, Y., Truong, A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.03.039 · Cited by 186
Sajuthi, S. P., DeFord, P., Li, Y., Jackson, N. D., Montgomery, M. T., Everman, J. L., Rios, C. L., Pruesse, E., Nolin, J. D., Plender, E. G., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18781-2 · Cited by 215
Lung megakaryocytes are immune modulatory cells
Pariser, D. N., Hilt, Z. T., Ture, S. K., Blick-Nitko, S. K., Looney, M. R., Cleary, S. J., Roman-Pagan, E., Saunders, J. A., Georas, S. N., Veazey, J. M., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci137377 · Cited by 186
Brassinosteroid gene regulatory networks at cellular resolution in the Arabidopsis root
Nolan, T. M., Vukašinović, N., Hsu, C., Zhang, J., Vanhoutte, I., Shahan, R., Taylor, I. W., Greenstreet, L., Heitz, M., Afanassiev, A., et al. (2023) · Science · doi:10.1126/science.adf4721 · Cited by 188
Single-cell atlas of the human brain vasculature across development, adulthood and disease
Wälchli, T., Ghobrial, M., Schwab, M., Takada, S., Zhong, H., Suntharalingham, S., Vetiska, S., Rodríguez, D. G., Wu, R., Rehrauer, H., et al. (2024) · Nature · doi:10.1038/s41586-024-07493-y · Cited by 217
Experimental Considerations for Single-Cell RNA Sequencing Approaches
Nguyen, Q. H., Pervolarakis, N., Nee, K., & Kessenbrock, K. (2018) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2018.00108 · Cited by 170
He, L., Davila-Velderrain, J., Sumida, T. S., Hafler, D. A., Kellis, M., & Kulminski, A. M. (2021) · Communications Biology · doi:10.1038/s42003-021-02146-6 · Cited by 181
Lineage dynamics of murine pancreatic development at single-cell resolution
Byrnes, L. E., Wong, D. M., Subramaniam, M., Meyer, N. P., Gilchrist, C. L., Knox, S. M., Tward, A. D., Ye, C., & Sneddon, J. B. (2018) · Nature Communications · doi:10.1038/s41467-018-06176-3 · Cited by 185
Dissecting primate early post-implantation development using long-term in vitro embryo culture
Niu, Y., Sun, N., Li, C., Lei, Y., Huang, Z., Wu, J., Si, C., Dai, X., Liu, C., Wei, J., et al. (2019) · Science · doi:10.1126/science.aaw5754 · Cited by 192
Profound Treg perturbations correlate with COVID-19 severity
Galván-Peña, S., Léon, J., Chowdhary, K., Michelson, D. A., Vijaykumar, B., Yang, L., Magnuson, A. M., Chen, F., Manickas-Hill, Z., Trocha, A. K., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2111315118 · Cited by 189
Verginadis, I. I., Avgousti, H., Monslow, J., Skoufos, G., Chinga, F., Kim, K., Leli, N. M., Karagounis, I. V., Bell, B. I., Velalopoulou, A. P., et al. (2022) · Nature Cell Biology · doi:10.1038/s41556-022-00918-8 · Cited by 191
Recent advances in droplet microfluidics for single-cell analysis
Jiang, Z., Shi, H., Tang, X., & Qin, J. (2023) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2023.116932 · Cited by 193
Sun, J., Zhao, J., Jiang, F., Wang, L., Xiao, Q., Han, F., Chen, J., Yuan, S., Wei, J., Larsson, S. C., et al. (2023) · Genome Medicine · doi:10.1186/s13073-023-01229-9 · Cited by 197
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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