The DOI matches its Crossref record; 81 of the 82 references it cites were found in a registry or an index, and 1 are not checked yet.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
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
Flagged first, then by citations
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 1,101–1,197 of 1,197
Reducing microglial lipid load enhances β amyloid phagocytosis in an Alzheimer’s disease mouse model
Wu, X., Miller, J. A., Lee, B. T. K., Wang, Y., & Ruedl, C. (2025) · Science Advances · doi:10.1126/sciadv.adq6038 · Cited by 67
Rodosthenous, T., Shahrezaei, V., & Evangelou, M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa530 · Cited by 67
Zhang, H., Ma, Y., Cheng, X., Wu, D., Huang, X., Chen, B., Ren, Y., Jiang, W., Tang, X., Bai, T., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abd1206 · Cited by 65
A thalamic-primary auditory cortex circuit mediates resilience to stress
Li, H.-Y., Zhu, M.-Z., Yuan, X.-R., Guo, Z.-X., Pan, Y.-D., Li, Y.-Q., & Zhu, X.-H. (2023) · Cell · doi:10.1016/j.cell.2023.02.036 · Cited by 64
Wang, L., Nie, R., Yu, Z., Xin, R., Zheng, C., Zhang, Z., Zhang, J., & Cai, J. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00244-4 · Cited by 62
Zhou, J., Chen, S., Wu, Y., Li, H., Zhang, B., Zhou, L., Hu, Y., Xiang, Z., Li, Z., Chen, N., et al. (2024) · Science Advances · doi:10.1126/sciadv.adh8601 · Cited by 63
An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types
Yao, Z., Liu, H., Xie, F., Fischer, S., Booeshaghi, A. S., Adkins, R. S., Aldridge, A. I., Ament, S. A., Pinto-Duarte, A., Bartlett, A., et al. (2020) · doi:10.1101/2020.02.29.970558 · Cited by 397
Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis
Mandric, I., Schwarz, T., Majumdar, A., Hou, K., Briscoe, L., Perez, R., Subramaniam, M., Hafemeister, C., Satija, R., Ye, C. J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19365-w · Cited by 62
Self-citationUncovering axes of variation among single-cell cancer specimens
Chen, W. S., Zivanovic, N., van Dijk, D., Wolf, G., Bodenmiller, B., & Krishnaswamy, S. (2020) · Nature Methods · doi:10.1038/s41592-019-0689-z · Cited by 48
Formate Supplementation Enhances Antitumor CD8+ T-cell Fitness and Efficacy of PD-1 Blockade
Rowe, J. H., Elia, I., Shahid, O., Gaudiano, E. F., Sifnugel, N. E., Johnson, S., Reynolds, A. G., Fung, M. E., Joshi, S., LaFleur, M. W., et al. (2023) · Cancer Discovery · doi:10.1158/2159-8290.cd-22-1301 · Cited by 59
Xu, Z., Sziraki, A., Lee, J., Zhou, W., & Cao, J. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01948-9 · Cited by 52
Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq
Mylka, V., Matetovici, I., Poovathingal, S., Aerts, J., Vandamme, N., Seurinck, R., Verstaen, K., Hulselmans, G., Van den Hoecke, S., Scheyltjens, I., et al. (2022) · Genome Biology · doi:10.1186/s13059-022-02628-8 · Cited by 53
Tian, S., Chen, X., Wu, W., Lin, H., Qing, X., Liu, S., Wang, B., Xiao, Y., Shao, Z., & Peng, Y. (2024) · Experimental & Molecular Medicine · doi:10.1038/s12276-024-01168-4 · Cited by 53
Ho, R., Workman, M. J., Mathkar, P., Wu, K., Kim, K. J., O’Rourke, J. G., Kellogg, M., Montel, V., Banuelos, M. G., Arogundade, O. A., et al. (2021) · Cell Systems · doi:10.1016/j.cels.2020.10.010 · Cited by 62
Loss of Kat2a enhances transcriptional noise and depletes acute myeloid leukemia stem-like cells
Domingues, A. F., Kulkarni, R., Giotopoulos, G., Gupta, S., Vinnenberg, L., Arede, L., Foerner, E., Khalili, M., Adao, R. R., Johns, A., et al. (2020) · eLife · doi:10.7554/elife.51754 · Cited by 52
Claes, C., England, W. E., Danhash, E. P., Kiani Shabestari, S., Jairaman, A., Chadarevian, J. P., Hasselmann, J., Tsai, A. P., Coburn, M. A., Sanchez, J., et al. (2022) · Alzheimer's & Dementia · doi:10.1002/alz.12577 · Cited by 52
Uriarte Huarte, O., Kyriakis, D., Heurtaux, T., Pires-Afonso, Y., Grzyb, K., Halder, R., Buttini, M., Skupin, A., Mittelbronn, M., & Michelucci, A. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.639613 · Cited by 52
A human brain vascular atlas reveals diverse cell mediators of Alzheimer’s disease risk
Yang, A. C., Vest, R. T., Kern, F., Lee, D. P., Maat, C. A., Losada, P. M., Chen, M. B., Agam, M., Schaum, N., Khoury, N., et al. (2021) · doi:10.1101/2021.04.26.441262 · Cited by 48
sc2GWAS: a comprehensive platform linking single cell and GWAS traits of human
Yin, M., Feng, C., Yu, Z., Zhang, Y., Li, Y., Wang, X., Song, C., Guo, M., & Li, C. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1008 · Cited by 51
Xing, Q. R., El Farran, C. A., Gautam, P., Chuah, Y. S., Warrier, T., Toh, C. X. D., Kang, N. Y., Sugii, S., Chang, Y. T., Xu, J., et al. (2020) · Science Advances · doi:10.1126/sciadv.aba1190 · Cited by 51
ClusterMap: compare multiple single cell RNA-Seq datasets across different experimental conditions
Gao, X., Hu, D., Gogol, M., & Li, H. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz024 · Cited by 47
Chen, W. S., Liang, Y., Zong, M., Liu, J. J., Kaneko, K., Hanley, K. L., Zhang, K., & Feng, G.-S. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109974 · Cited by 50
Wei, C., Sun, W., Shen, K., Zhong, J., Liu, W., Gao, Z., Xu, Y., Wang, L., Hu, T., Ren, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43980-y · Cited by 49
Sex specific molecular networks and key drivers of Alzheimer’s disease
Guo, L., Cao, J., Hou, J., Li, Y., Huang, M., Zhu, L., Zhang, L., Lee, Y., Duarte, M. L., Zhou, X., et al. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00624-5 · Cited by 49
“The principal component analysis (PCA) was performed with significant principal components determined by a JackStraw permutation procedure to select cell clustering using Seurat’s graph-based clustering approaches [39].”
MethodToral-Rios, D., Long, J. M., Ulrich, J. D., Yu, J., Strickland, M. R., Han, X., Holtzman, D. M., Cashikar, A. G., & Paul, S. M. (2024) · Journal of Experimental Medicine · doi:10.1084/jem.20232000 · Cited by 47
Single cell-derived spheroids capture the self-renewing subpopulations of metastatic ovarian cancer
Velletri, T., Villa, C. E., Cilli, D., Barzaghi, B., Lo Riso, P., Lupia, M., Luongo, R., López-Tobón, A., De Simone, M., Bonnal, R. J. P., et al. (2022) · Cell Death & Differentiation · doi:10.1038/s41418-021-00878-w · Cited by 48
High-resolution characterization of gene function using single-cell CRISPR tiling screen
Yang, L., Chan, A. K. N., Miyashita, K., Delaney, C. D., Wang, X., Li, H., Pokharel, S. P., Li, S., Li, M., Xu, X., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24324-0 · Cited by 45
CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks
Buterez, D., Bica, I., Tariq, I., Andrés-Terré, H., & Liò, P. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab804 · Cited by 44
Single-cell analysis of senescent epithelia reveals targetable mechanisms promoting fibrosis
O’Sullivan, E. D., Mylonas, K. J., Bell, R., Carvalho, C., Baird, D. P., Cairns, C., Gallagher, K. M., Campbell, R., Docherty, M., Laird, A., et al. (2022) · JCI Insight · doi:10.1172/jci.insight.154124 · Cited by 43
Autoimmunity affecting the biliary tract fuels the immunosurveillance of cholangiocarcinoma
Paillet, J., Plantureux, C., Lévesque, S., Le Naour, J., Stoll, G., Sauvat, A., Caudana, P., Tosello Boari, J., Bloy, N., Lachkar, S., et al. (2021) · Journal of Experimental Medicine · doi:10.1084/jem.20200853 · Cited by 41
Li, Z., Chen, X., Vong, J. S. L., Zhao, L., Huang, J., Yan, L. Y. C., Ip, B., Wing, Y. K., Lai, H.-M., Mok, V. C. T., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-02208-9 · Cited by 42
Xu, Y., Deng, C., Chen, H., Song, Y., Xu, H., Song, G., Wang, X., Luo, T., Chen, W., Ma, J., et al. (2024) · Cancer Research · doi:10.1158/0008-5472.can-23-1307 · Cited by 41
Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining
Alessandri, L., Cordero, F., Beccuti, M., Licheri, N., Arigoni, M., Olivero, M., Di Renzo, M. F., Sapino, A., & Calogero, R. (2021) · npj Systems Biology and Applications · doi:10.1038/s41540-020-00162-6 · Cited by 40
Qiu, Y., Hou, Y., Gohel, D., Zhou, Y., Xu, J., Bykova, M., Yang, Y., Leverenz, J. B., Pieper, A. A., Nussinov, R., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114128 · Cited by 40
In vivo single-cell CRISPR uncovers distinct TNF programmes in tumour evolution
Renz, P. F., Ghoshdastider, U., Baghai Sain, S., Valdivia-Francia, F., Khandekar, A., Ormiston, M., Bernasconi, M., Duré, C., Kretz, J. A., Lee, M., et al. (2024) · Nature · doi:10.1038/s41586-024-07663-y · Cited by 40
Single‐cell RNAseq provides insight into altered immune cell populations in human fracture nonunions
Avin, K. G., Dominguez, J. M., Chen, N. X., Hato, T., Myslinski, J. J., Gao, H., Liu, Y., McKinley, T. O., Brown, K. M., Moe, S. M., et al. (2023) · Journal of Orthopaedic Research · doi:10.1002/jor.25452 · Cited by 39
An interferon gamma response signature links myocardial aging and immunosenescence
Ashour, D., Rebs, S., Arampatzi, P., Saliba, A.-E., Dudek, J., Schulz, R., Hofmann, U., Frantz, S., Cochain, C., Streckfuß-Bömeke, K., et al. (2023) · Cardiovascular Research · doi:10.1093/cvr/cvad068 · Cited by 39
Distinct senotypes in p16- and p21-positive cells across human and mouse aging tissues
Saul, D., Jurk, D., Doolittle, M. L., Kosinsky, R. L., Han, Y., Zhang, X., Franco, A. C., Kim, S. Y., Wyles, S. P., Prakash, Y. S., et al. (2025) · The EMBO Journal · doi:10.1038/s44318-025-00601-2 · Cited by 45
Altered patterning of trisomy 21 interneuron progenitors
Giffin-Rao, Y., Sheng, J., Strand, B., Xu, K., Huang, L., Medo, M., Risgaard, K. A., Dantinne, S., Mohan, S., Keshan, A., et al. (2022) · Stem Cell Reports · doi:10.1016/j.stemcr.2022.05.001 · Cited by 38
Wagner, S., Brierley, D. I., Leeson-Payne, A., Jiang, W., Chianese, R., Lam, B. Y. H., Dowsett, G. K. C., Cristiano, C., Lyons, D., Reimann, F., et al. (2023) · Molecular Metabolism · doi:10.1016/j.molmet.2022.101665 · Cited by 32
Funk, O. H., Qalieh, Y., Doyle, D. Z., Lam, M. M., & Kwan, K. Y. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2116956119 · Cited by 33
Single cell epigenomic atlas of the developing human brain and organoids
Ziffra, R. S., Kim, C. N., Wilfert, A., Turner, T. N., Haeussler, M., Casella, A. M., Przytycki, P. F., Kreimer, A., Pollard, K. S., Ament, S. A., et al. (2019) · doi:10.1101/2019.12.30.891549 · Cited by 33
Micali, N., Kim, S.-K., Diaz-Bustamante, M., Stein-O’Brien, G., Seo, S., Shin, J.-H., Rash, B. G., Ma, S., Wang, Y., Olivares, N. A., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107599 · Cited by 31
Somatic mutations alter the differentiation outcomes of iPSC-derived neurons
Puigdevall, P., Jerber, J., Danecek, P., Castellano, S., & Kilpinen, H. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100280 · Cited by 29
Unstable regulatory T cells, enriched for naïve and Nrp1negcells, are purged after fate challenge
Junius, S., Mavrogiannis, A. V., Lemaitre, P., Gerbaux, M., Staels, F., Malviya, V., Burton, O., Gergelits, V., Singh, K., Tito Tadeo, R. Y., et al. (2021) · Science Immunology · doi:10.1126/sciimmunol.abe4723 · Cited by 30
Occult polyclonality of preclinical pancreatic cancer models drives in vitro evolution
Monberg, M. E., Geiger, H., Lee, J. J., Sharma, R., Semaan, A., Bernard, V., Wong, J., Wang, F., Liang, S., Swartzlander, D. B., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31376-3 · Cited by 30
Ag-driven CD8+ T cell clonal expansion is a prominent feature of MASH in humans and mice
Burtis, A. E. C., DeNicola, D. M. C., Ferguson, M. E., Santos, R. G., Pinilla, C., Kriss, M. S., Orlicky, D. J., Tamburini, B. A. J., Gillen, A. E., & Burchill, M. A. (2025) · Hepatology · doi:10.1097/hep.0000000000000971 · Cited by 30
Ma, Y., Guo, C., Wang, X., Wei, X., & Ma, J. (2023) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-023-02804-z · Cited by 29
Single-Cell RNA Sequencing for Studying Human Cancers
Aran, D. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020722-091857 · Cited by 31
Thurman, A. L., Ratcliff, J. A., Chimenti, M. S., & Pezzulo, A. A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab337 · Cited by 27
Barry, T., Mason, K., Roeder, K., & Katsevich, E. (2024) · Genome Biology · doi:10.1186/s13059-024-03254-2 · Cited by 28
Spatial transcriptome sequencing of FFPE tissues at cellular level
Liu, Y., Enninful, A., Deng, Y., & Fan, R. (2020) · doi:10.1101/2020.10.13.338475 · Cited by 26
Venugopal, G., Bird, J. T., Washam, C. L., Roys, H., Bowlin, A., Byrum, S. D., & Weinkopff, T. (2022) · PLOS Neglected Tropical Diseases · doi:10.1371/journal.pntd.0010518 · Cited by 26
Ji, T., Zhang, L., Deng, M., Huang, S., Wang, Y., Pham, T. T., Smith, A. A., Sridhar, V., Cabernard, C., Wang, J., et al. (2019) · Disease Models & Mechanisms · doi:10.1242/dmm.040147 · Cited by 25
Balakumar, A., Das, D., Datta, A., Mishra, A., Bryak, G., Ganesh, S. M., Netea, M. G., Kumar, V., Lionakis, M. S., Arora, D., et al. (2024) · PLOS Pathogens · doi:10.1371/journal.ppat.1012699 · Cited by 25
Using blood transcriptome analysis for Alzheimer's disease diagnosis and patient stratification
Zhong, H., Zhou, X., Uhm, H., Jiang, Y., Cao, H., Chen, Y., Mak, T. T. W., Lo, R. M. N., Wong, B. W. Y., Cheng, E. Y. L., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.13691 · Cited by 25
Unravelling the Shared Genetic Mechanisms Underlying 18 Autoimmune Diseases Using a Systems Approach
Gokuladhas, S., Schierding, W., Golovina, E., Fadason, T., & O’Sullivan, J. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.693142 · Cited by 25
Hachey, S. J., Hatch, C. J., Gaebler, D., Mocherla, A., Nee, K., Kessenbrock, K., & Hughes, C. C. W. (2024) · Breast Cancer Research · doi:10.1186/s13058-023-01760-y · Cited by 24
Dysfunctional purinergic signaling correlates with disease severity in COVID-19 patients
Pietrobon, A. J., Andrejew, R., Custódio, R. W. A., Oliveira, L. D. M., Scholl, J. N., Teixeira, F. M. E., de Brito, C. A., Glaser, T., Kazmierski, J., Goffinet, C., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1012027 · Cited by 23
The Nr4a family regulates intrahepatic Treg proliferation and liver fibrosis in MASLD models
Aki, D., Hayakawa, T., Srirat, T., Shichino, S., Ito, M., Saitoh, S.-I., Mise-Omata, S., & Yoshimura, A. (2024) · Journal of Clinical Investigation · doi:10.1172/jci175305 · Cited by 22
Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2018) · doi:10.1101/478503 · Cited by 21
T–B cell cooperation in ectopic lymphoid follicles propagates CNS autoimmunity
Kolz, A., de la Rosa, C., Syma, I. J., McGrath, S., Kavaka, V., Schmitz, R., Thomann, A. S., Kerschensteiner, M., Beltran, E., Kawakami, N., et al. (2025) · Science Immunology · doi:10.1126/sciimmunol.adn2784 · Cited by 20
Xi, C., Yan, Z., Bai, D., Zhang, Y., Wang, B., Han, X., Wu, L., Shi, X., Hu, Z., Tang, M., et al. (2024) · Protein & Cell · doi:10.1093/procel/pwae006 · Cited by 18
Chen, Z., Ye, L., Zhu, M., Xia, C., Fan, J., Chen, H., Li, Z., & Mou, S. (2024) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-024-05118-1 · Cited by 17
Genomic Insights into the Population History and Adaptive Traits of Latin American Criollo Cattle
Ward, J. A., Ng'ang'a, S. I., Randhawa, I. A. S., McHugo, G. P., O'Grady, J. F., Flórez, J. M., Browne, J. A., Pérez O’Brien, A. M., Landaeta-Hernández, A. J., Garcia, J. F., et al. (2024) · Royal Society Open Science · doi:10.1098/rsos.231388 · Cited by 17
Lyu, T., Liu, Y., Li, B., Xu, R., Guo, J., & Zhu, D. (2023) · Biology Direct · doi:10.1186/s13062-023-00390-w · Cited by 18
Inhibiting USP16 rescues stem cell aging and memory in an Alzheimer’s model
Reinitz, F., Chen, E. Y., Nicolis di Robilant, B., Chuluun, B., Antony, J., Jones, R. C., Gubbi, N., Lee, K., Ho, W. H. D., Kolluru, S. S., et al. (2022) · eLife · doi:10.7554/elife.66037 · Cited by 18
Fernandez, B., Passanisi, V. J., Ashraf, H. M., & Spencer, S. L. (2025) · Nature Communications · doi:10.1038/s41467-025-66836-z · Cited by 18
Primary cilia-mediated regulation of microglial secretion in Alzheimer’s disease
Yeo, S., Jang, J., Jung, H. J., Lee, H., & Choe, Y. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1250335 · Cited by 18
Mikkilineni, L., Natrakul, D. A., Lam, N., Manasanch, E. E., Mann, J., Weissler, K. A., Wong, N., Brudno, J. N., Goff, S. L., Yang, J. C., et al. (2024) · Molecular Therapy · doi:10.1016/j.ymthe.2023.12.018 · Cited by 16
Tanimoto, Y., Kakinuma, H., Aoki, R., Shiraki, T., Higashijima, S.-I., & Okamoto, H. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113916 · Cited by 16
Helios represses megakaryocyte priming in hematopoietic stem and progenitor cells
Cova, G., Taroni, C., Deau, M.-C., Cai, Q., Mittelheisser, V., Philipps, M., Jung, M., Cerciat, M., Le Gras, S., Thibault-Carpentier, C., et al. (2021) · Journal of Experimental Medicine · doi:10.1084/jem.20202317 · Cited by 17
Bi, X.-P., 深圳518083，中国, 深., BGI-Shenzhen, S., Zhang, G.-J., 650223，中国, 中. 昆., 650223，中国, 中. 昆., Villum Center for Biodiversity Genomics, S. F. E. A. E., Center for Excellence in Animal Evolution and Genetics, C. A. O. S., State Key Laboratory of Genetic Resources and Evolution, K. I. O. Z., & Villum Center for Biodiversity Genomics, S. F. E. A. E. (2021) · Zoological Research · doi:10.24272/j.issn.2095-8137.2021.066 · Cited by 15
Targeting the Immune-Fibrosis Axis in Myocardial Infarction and Heart Failure
Amrute, J. M., Luo, X., Penna, V., Bredemeyer, A., Yamawaki, T., Heo, G. S., Shi, S., Koenig, A., Yang, S., Kadyrov, F., et al. (2022) · bioRxiv · doi:10.1101/2022.10.17.512579 · Cited by 14
Frank, A. K., Chung, B. K., De Novales, M. L. L., Engesæter, L. K., Hoyle, H. W., Øgaard, J., Heslop, J., Karlsen, T. H., Tysoe, O., Brevini, T., et al. (2024) · Digestive Diseases and Sciences · doi:10.1007/s10620-024-08570-y · Cited by 14
Cell diversity and plasticity during atrioventricular heart valve EMTs
Lotto, J., Cullum, R., Drissler, S., Arostegui, M., Garside, V. C., Fuglerud, B. M., Clement-Ranney, M., Thakur, A., Underhill, T. M., & Hoodless, P. A. (2023) · Nature Communications · doi:10.1038/s41467-023-41279-6 · Cited by 13
“PCA was done using the RunPCA function in Seurat 85 .”
MethodInfluentialMulti-modal quantification of pathway activity with MAYA
Landais, Y., & Vallot, C. (2023) · Nature Communications · doi:10.1038/s41467-023-37410-2 · Cited by 9
“To quantify the inter-patient overlap between clusters of similar cell types, we computed the Shannon Diversity Index (SDI) for both methods as well as for clusters obtained with the reference integration tool Harmony 42 and an integration method based on Canonical Correlation Analysis 43 (CCA) (Supplementary Fig.”
MethodInfluentialXi, Y., Zhao, Z., Zhou, Y., Yin, C., Li, Y., Xu, X., Jin, W., Lin, C., Zhao, K., Kuang, J., et al. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102529 · Cited by 12
Jung, S., Singh, K., & del Sol, A. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa283 · Cited by 9
Multi-omic analyses in immune cell development with lessons learned from T cell development
Cordes, M., Pike-Overzet, K., Van Den Akker, E. B., Staal, F. J. T., & Canté-Barrett, K. (2023) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2023.1163529 · Cited by 9
Philtjens, S., Turnbull, M. T., Thedy, B. P., Moon, Y., & Kim, J. (2020) · doi:10.1101/2020.04.27.064881 · Cited by 7
“Subsequent data analysis was performed using the R (v.3.5.2) package Seurat v.3.0.2 (Butler et al., 2018; Stuart et al., 2019).”
MethodInfluentialThe RNA exosome maintains cellular RNA homeostasis by controlling transcript abundance in the brain
Higginson, L. A., Wang, X., He, K., Torstrick, M., Kim, M., Wijeratne, H. R. S., Runnebohm, A. M., Benayoun, B. A., MacLean, A., Chanfreau, G. F., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2025.116729 · Cited by 7
Defining Cardiac Recovery at Single Cell Resolution
Amrute, J. M., Lai, L., Ma, P., Koenig, A. L., Kamimoto, K., Bredemeyer, A., Shankar, T. S., Kuppe, C., Kadyrov, F. F., Schulte, L. J., et al. (2022) · doi:10.1101/2022.09.11.507463 · Cited by 6
Huang, X., Liu, Y., Wang, Q., Rehman, H. M., Horváth, D., Zhou, S., Fu, R., Zhang, L., Szöllősi, A. G., & Li, Z. (2023) · BMC Oral Health · doi:10.1186/s12903-023-03181-x · Cited by 6
RetractedTBX3 is dynamically expressed in pancreatic organogenesis and fine-tunes regeneration
Melzer, M. K., Schirge, S., Gout, J., Arnold, F., Srinivasan, D., Burtscher, I., Allgöwer, C., Mulaw, M., Zengerling, F., Günes, C., et al. (2023) · BMC Biology · doi:10.1186/s12915-023-01553-x · Cited by 5
Computational challenges of cell cycle analysis using single cell transcriptomics
Chervov, A., & Zinovyev, A. (2022) · arXiv · doi:10.48550/arxiv.2208.05229 · Cited by 4
“, Seurat [5], Scanpy [6]) to analyse the cell cycle dynamics and are there better alternatives? The brief analysis of the Seurat/Scanpy features related to cell cycle can be found in section 4.”
BackgroundESRP2–microRNA-122 axis promotes the postnatal onset of liver polyploidization and maturation
Bangru, S., Chen, J., Baker, N., Das, D., Chembazhi, U. V., Derham, J. M., Chorghade, S., Arif, W., Alencastro, F., Duncan, A. W., et al. (2025) · Genes & Development · doi:10.1101/gad.352129.124 · Cited by 4
Agboraw, E., Haese-Hill, W., Hentzschel, F., Briggs, E., Aghabi, D., Heawood, A., Harding, C. R., Shiels, B., Crouch, K., Somma, D., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf091 · Cited by 4
SINGLE CELL ANALYSIS, WHAT IS IN THE FUTURE?
Garmire, L. X., Yuan, G.-C., Fan, R., Yeo, G. W., & Quackenbush, J. (2018) · Biocomputing 2019 · doi:10.1142/9789813279827_0030 · Cited by 2
Kadyrov, F. F., Koenig, A. L., Amrute, J. M., Dun, H., Li, W., Weinheimer, C. J., Nigro, J. M., Kovacs, A., Bredemeyer, A. L., Lai, L., et al. (2022) · doi:10.1101/2022.08.04.502542 · Cited by 3
Type 1 interferon perturbates clonal competition by reshaping human blood development
Lama, C., Isakov, D., Rosenberg, S., Quijada-Álamo, M., Saurty-Seerunghen, M. S., Moein, S., Nozais, M., Abera, T.-A., Sakaguchi, O., Totwani, M., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02751-3 · Cited by 3
Self-citationIdentifying determinants of synaptic specificity by integrating connectomes and transcriptomes
Yoo, J., Dombrovski, M., Mirshahidi, P., Nern, A., LoCascio, S. A., Zipursky, S. L., & Kurmangaliyev, Y. Z. (2023) · doi:10.1101/2023.04.03.534791 · Cited by 39
Wang, Y., Zang, C., Li, Z., Guo, C. C., Lai, D., & Wei, P. (2026) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1013956 · Cited by 2
Immunosuppression and phenotypic plasticity in an atlas of human hepatocholangiocarcinoma
Li, Y., Xun, Z., Long, J., Sun, H., Yang, X., Wang, Y., Wang, Y., Xue, J., Zhang, N., Zhang, J., et al. (2024) · Hepatobiliary Surgery and Nutrition · doi:10.21037/hbsn-23-400 · Cited by 2
Yoo, Y.-E., Mandal, P., Tang, Z., Zhang, Z., Zhang, J., Chen, X., Robinson, M., Eaton, M., Deming, B., Halurkar, M., et al. (2025) · doi:10.1101/2025.09.16.676420 · Cited by 1
TBX3 advances the developmental chromatin landscape toward the hepatic fate
Stephan, T. L., Cullum, R., Drissler, S., Thomas, A., Fuglerud, B. M., Clement-Ranney, M., Lotto, J., Krentz, N. A. J., Duncan, S. A., & Hoodless, P. A. (2025) · Developmental Cell · doi:10.1016/j.devcel.2025.05.014 · Cited by 1
Lebeaupin, C., Donahue, K. L., Lal, S., Christensen, S. M., Asano, S., Wadsworth, M. H., Bound, K., De, S., McMahon, J., Schlerman, F., et al. (2025) · bioRxiv (preprint) · doi:10.64898/2025.12.17.694415
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
