Integrated analysis of single cell transcriptomic data across conditions, technologies, and species
Open accessPreprint — published as 10.1038/nbt.4096 · Paper page
Abstract
Single cell RNA-seq (scRNA-seq) has emerged as a transformative tool to discover and define cellular phenotypes. While computational scRNA-seq methods are currently well suited for experiments representing a single condition, technology, or species, analyzing multiple datasets simultaneously raises new challenges. In particular, traditional analytical workflows struggle to align subpopulations that are present across datasets, limiting the possibility for integrated or comparative analysis. Here, we introduce a new computational strategy for scRNA-seq alignment, utilizing common sources of variation to identify shared subpopulations between datasets as part of our R toolkit Seurat. We demonstrate our approach by aligning scRNA-seq datasets of PBMCs under resting and stimulated conditions, hematopoietic progenitors sequenced across 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 datasets, and can identify subpopulations that could not be detected by analyzing datasets independently. We anticipate that these methods will serve not only to correct for batch or technology-dependent effects, but also to facilitate general comparisons of scRNA-seq datasets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution. Availability: Installation instructions, documentation, and tutorials are available at http://www.satijalab.org/seurat
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
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?
Using a publicly available adult dataset, the study identified 14 inhibitory interneuron subpopulations that encompass known anatomically and physiologically defined subtypes.
“utilizing a publicly available dataset22,23, we identified 14 inhibitory interneuron subpopulations that encompass known anatomically and physiologically defined subtypes24,25”
From Developmental diversification of cortical inhibitory interneurons (2018) · cites this paper as [25]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 · see citing works
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 · see citing works
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 · cited 1× · see citing works
“(1-7) (1-7) (8) (8) (12-13) (12-13) (18) (18) (9-10) (9-10) (11) (11) (14-15) (14-15) (16-17) (16-17) Er yth Mk Bas DC ono eu Eo LM PP .”
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 · see citing works
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 · cited 1× · see citing works
“(1-7) (1-7) (8) (8) (12-13) (12-13) (18) (18) (9-10) (9-10) (11) (11) (14-15) (14-15) (16-17) (16-17) Er yth Mk Bas DC ono eu Eo LM PP .”
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 · cited 1× · see citing works
“(1-7) (1-7) (8) (8) (12-13) (12-13) (18) (18) (9-10) (9-10) (11) (11) (14-15) (14-15) (16-17) (16-17) Er yth Mk Bas DC ono eu Eo LM PP .”
Self-citationRelations Between Two Sets of Variates
Hotelling, H. (1936) · Springer series in statistics · doi:10.1007/978-1-4612-4380-9_14 · Cited by 7,029 · see citing works
RELATIONS BETWEEN TWO SETS OF VARIATES
Hotelling, H. (1936) · Biometrika · doi:10.1093/biomet/28.3-4.321 · Cited by 5,429 · see citing works
Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834 · see citing works
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356 · see citing works
Canonical Correlation Analysis: An Overview with Application to Learning Methods
Hardoon, D. R., Szedmák, S., & Shawe-Taylor, J. S. (2004) · Neural Computation · doi:10.1162/0899766042321814 · Cited by 3,297 · see citing works
Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A. K., Slichter, C. K., Miller, H. W., McElrath, M. J., Prlic, M., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0844-5 · Cited by 3,159 · cited 1× · see citing works
“(1-7) (1-7) (8) (8) (12-13) (12-13) (18) (18) (9-10) (9-10) (11) (11) (14-15) (14-15) (16-17) (16-17) Er yth Mk Bas DC ono eu Eo LM PP .”
(1994) · Knowledge Discovery and Data Mining · Cited by 3,137 · see citing works
A survey of best practices for RNA-seq data analysis
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794 · see citing works
Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors
Villani, A.-C., Satija, R., Reynolds, G., Sarkizova, S., Shekhar, K., Fletcher, J., Griesbeck, M., Butler, A., Zheng, S., Lazo, S., et al. (2017) · Science · doi:10.1126/science.aah4573 · Cited by 2,413 · cited 1× · see citing works
“(1-7) (1-7) (8) (8) (12-13) (12-13) (18) (18) (9-10) (9-10) (11) (11) (14-15) (14-15) (16-17) (16-17) Er yth Mk Bas DC ono eu Eo LM PP .”
Self-citationRegev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232 · cited 1× · see citing works
“(1-7) (1-7) (8) (8) (12-13) (12-13) (18) (18) (9-10) (9-10) (11) (11) (14-15) (14-15) (16-17) (16-17) Er yth Mk Bas DC ono eu Eo LM PP .”
Self-citationAncient DNA and the rewriting of human history: be sparing with Occam’s razor
Haber, M., Mezzavilla, M., Xue, Y., & Tyler-Smith, C. (2016) · Genome Biology · doi:10.1186/s13059-015-0866-z · Cited by 1,924 · see citing works
GOplot: an R package for visually combining expression data with functional analysis
Walter, W., Sánchez‐Cabo, F., & Ricote, M. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv300 · Cited by 1,760 · see citing works
Witten, D., Tibshirani, R. J., & Hastie, T. (2009) · Biostatistics · doi:10.1093/biostatistics/kxp008 · Cited by 1,580 · see citing works
Baron, M., Veres, A., Wolock, S. L., Faust, A. L., Gaujoux, R., Vetere, A., Ryu, J. H., Wagner, B. K., Shen-Orr, S. S., Klein, A. M., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.08.011 · Cited by 1,508 · see citing works
SC3: consensus clustering of single-cell RNA-seq data
Kiselev, V. Y., Kirschner, K., Schaub, M. T., Andrews, T., Yiu, A., Chandra, T., Natarajan, K. N., Reik, W., Barahona, M., Green, A. R., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4236 · Cited by 1,500 · see citing works
Comparative Analysis of Single-Cell RNA Sequencing Methods
Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., & Enard, W. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.01.023 · Cited by 1,494 · see citing works
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., & al, E. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162597 · Cited by 1,457 · see citing works
SC3 - consensus clustering of single-cell RNA-Seq dataPreprint
Kiselev, V., Kirschner, K., Schaub, M. T., et al. (2016) · Nature Methods · doi:10.1101/036558 · Cited by 1,357 · see citing works
Bayesian approach to single-cell differential expression analysis
Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014) · Nature Methods · doi:10.1038/nmeth.2967 · Cited by 1,348 · cited 1× · see citing works
“(1-7) (1-7) (8) (8) (12-13) (12-13) (18) (18) (9-10) (9-10) (11) (11) (14-15) (14-15) (16-17) (16-17) Er yth Mk Bas DC ono eu Eo LM PP .”
Identification of Flt3+ Lympho-Myeloid Stem Cells Lacking Erythro-Megakaryocytic Potential
Adolfsson, J., Månsson, R., Buza-Vidas, N., Hultquist, A., Liuba, K., Jensen, C. T., Bryder, D., Yang, L., Borge, O. J., Thorén, L., et al. (2005) · Cell · doi:10.1016/j.cell.2005.02.013 · Cited by 1,229 · see citing works
Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
Shekhar, K., Lapan, S. W., Whitney, I. E., Tran, N. M., Macosko, E. Z., Kowalczyk, M., Adiconis, X., Levin, J. Z., Nemesh, J., Goldman, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.07.054 · Cited by 1,176 · see citing works
Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors
Paul, F., Arkin, Y., Giladi, A., Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Winter, D., Lara-Astiaso, D., Gury, M., Weiner, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.013 · Cited by 1,082 · see citing works
Inflammation in Alzheimer Disease--A Brief Review of the Basic Science and Clinical Literature
Wyss-Coray, T., & Rogers, J. (2012) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a006346 · Cited by 975 · see citing works
Highly Multiplexed Subcellular RNA Sequencing in Situ
Lee, J. H., Daugharthy, E. R., Scheiman, J., Kalhor, R., Yang, J. L., Ferrante, T. C., Terry, R., Jeanty, S. S. F., Li, C., Amamoto, R., et al. (2014) · Science · doi:10.1126/science.1250212 · Cited by 973 · see citing works
Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain
Lake, B. B., Ai, R., Kaeser, G. E., Salathia, N. S., Yung, Y. C., Liu, R., Wildberg, A., Gao, D., Fung, H.-L., Chen, S., et al. (2016) · Science · doi:10.1126/science.aaf1204 · Cited by 957 · see citing works
Comparative Analysis of Single-Cell RNA Sequencing MethodsPreprint
Ziegenhain, C., Vieth, B., Parekh, S., et al. (2016) · bioRxiv · doi:10.1101/035758 · Cited by 951 · see citing works
Bendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Simonds, E. F., Chen, T. J., Shenfeld, D. K., Nolan, G. P., & Pe’er, D. (2014) · Cell · doi:10.1016/j.cell.2014.04.005 · Cited by 906 · cited 1× · see citing works
“(1-7) (1-7) (8) (8) (12-13) (12-13) (18) (18) (9-10) (9-10) (11) (11) (14-15) (14-15) (16-17) (16-17) Er yth Mk Bas DC ono eu Eo LM PP .”
Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput
Gierahn, T. M., Wadsworth, M. H., Hughes, T. K., Bryson, B. D., Butler, A., Satija, R., Fortune, S. M., Love, J. C., & Shalek, A. K. (2017) · Nature Methods · doi:10.1038/nmeth.4179 · Cited by 855 · see citing works
Self-citationA single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation
Nestorowa, S., Hamey, F. K., Pijuan Sala, B., Diamanti, E., Shepherd, M., Laurenti, E., Wilson, N. K., Kent, D. G., & Göttgens, B. (2016) · Blood · doi:10.1182/blood-2016-05-716480 · Cited by 768 · see citing works
Single-cell barcoding and sequencing using droplet microfluidics
Žilionis, R., Nainys, J., Veres, A., Savova, V., Zemmour, D., Klein, A. M., & Mažutis, L. (2016) · Nature Protocols · doi:10.1038/nprot.2016.154 · Cited by 715 · cited 2× · see citing works
“(1-7) (1-7) (8) (8) (12-13) (12-13) (18) (18) (9-10) (9-10) (11) (11) (14-15) (14-15) (16-17) (16-17) Er yth Mk Bas DC ono eu Eo LM PP .”
MethodVisualization and analysis of single-cell RNA-seq data by kernel-based similarity learning
Wang, B., Zhu, J., Pierson, E., Ramazzotti, D., & Batzoglou, S. (2017) · Nature Methods · doi:10.1038/nmeth.4207 · Cited by 706 · see citing works
Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learningPreprint
Wang, B., Zhu, J., Pierson, E., et al. (2016) · Nature Methods · doi:10.1101/052225 · Cited by 658 · cited 1× · see citing works
“(1-7) (1-7) (8) (8) (12-13) (12-13) (18) (18) (9-10) (9-10) (11) (11) (14-15) (14-15) (16-17) (16-17) Er yth Mk Bas DC ono eu Eo LM PP .”
Power analysis of single-cell RNA-sequencing experiments
Svensson, V., Natarajan, K. N., Ly, L.-H., Miragaia, R. J., Labalette, C., Macaulay, I. C., Cvejic, A., & Teichmann, S. A. (2017) · Nature Methods · doi:10.1038/nmeth.4220 · Cited by 568 · see citing works
Identification of cell types from single-cell transcriptomes using a novel clustering method
Xu, C., & Su, Z. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv088 · Cited by 559 · see citing works
Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data
Witten, D., & Tibshirani, R. J. (2009) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1470 · Cited by 551 · see citing works
CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data
Lin, P., Troup, M., & Ho, J. W. K. (2017) · Genome biology · doi:10.1186/s13059-017-1188-0 · Cited by 529 · see citing works
The Unfolded Protein Response: A Pathway That Links Insulin Demand with β-Cell Failure and Diabetes
Scheuner, D., & Kaufman, R. J. (2008) · Endocrine Reviews · doi:10.1210/er.2007-0039 · Cited by 525 · see citing works
Jiang, H.-Y., Wek, S. A., McGrath, B. C., Lü, D., Hai, T., Harding, H. P., Wang, X., Ron, D., Cavener, D. R., & Wek, R. C. (2004) · Molecular and Cellular Biology · doi:10.1128/mcb.24.3.1365-1377.2004 · Cited by 504 · see citing works
Defining the three cell lineages of the human blastocyst by single-cell RNA-seq
Blakeley, P., Fogarty, N. M. E., del Valle, I., Wamaitha, S. E., Hu, T. X., Elder, K. T., Snell, P., Christie, L., Robson, P., & Niakan, K. K. (2015) · Development · doi:10.1242/dev.123547 · Cited by 466 · cited 1× · see citing works
“(1-7) (1-7) (8) (8) (12-13) (12-13) (18) (18) (9-10) (9-10) (11) (11) (14-15) (14-15) (16-17) (16-17) Er yth Mk Bas DC ono eu Eo LM PP .”
Heterogeneous Domain Adaptation Using Manifold Alignment
Wang, C., & Mahadevan, S. (2011) · International Joint Conference on Artificial Intelligence · doi:10.5591/978-1-57735-516-8/ijcai11-259 · Cited by 458
Single-Cell Resolution of Temporal Gene Expression during Heart Development
DeLaughter, D. M., Bick, A. G., Wakimoto, H., McKean, D. M., Gorham, J., Kathiriya, I. S., Hinson, J. T., Homsy, J. G., Gray, J. M., Pu, W. T., et al. (2016) · Developmental Cell · doi:10.1016/j.devcel.2016.10.001 · Cited by 416 · cited 1× · see citing works
“(1-7) (1-7) (8) (8) (12-13) (12-13) (18) (18) (9-10) (9-10) (11) (11) (14-15) (14-15) (16-17) (16-17) Er yth Mk Bas DC ono eu Eo LM PP .”
Heterogeneous Domain Adaptation Using Manifold Alignment
Wang, C., & Mahadevan, S. (2011) · Cited by 400 · see citing works
High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin
Achim, K., Pettit, J.-B., Saraiva, L. R., Gavriouchkina, D., Larsson, T., Arendt, D., & Marioni, J. C. (2015) · Nature Biotechnology · doi:10.1038/nbt.3209 · Cited by 395 · cited 1× · see citing works
“(1-7) (1-7) (8) (8) (12-13) (12-13) (18) (18) (9-10) (9-10) (11) (11) (14-15) (14-15) (16-17) (16-17) Er yth Mk Bas DC ono eu Eo LM PP .”
Defining the three cell lineages of the human blastocyst by single-cell RNA-seq
Blakeley, P., Fogarty, N. M. E., del Valle, I., Wamaitha, S. E., Hu, T. X., Elder, K. T., Snell, P., Christie, L., Robson, P., & Niakan, K. K. (2015) · Development · doi:10.1242/dev.131235 · Cited by 316 · see citing works
Genome-wide RNA Tomography in the Zebrafish Embryo
Junker, J. P., Noël, E. S., Guryev, V., Peterson, K. A., Shah, G., Huisken, J., McMahon, A. P., Berezikov, E., Bakkers, J., & van Oudenaarden, A. (2014) · Cell · doi:10.1016/j.cell.2014.09.038 · Cited by 314 · see citing works
Single-cell analysis reveals transcriptional heterogeneity of neural progenitors in human cortex
Johnson, M. B., Wang, P. P., Atabay, K. D., Murphy, E. A., Doan, R. N., Hecht, J. L., & Walsh, C. A. (2015) · Nature Neuroscience · doi:10.1038/nn.3980 · Cited by 288 · cited 1× · see citing works
“(1-7) (1-7) (8) (8) (12-13) (12-13) (18) (18) (9-10) (9-10) (11) (11) (14-15) (14-15) (16-17) (16-17) Er yth Mk Bas DC ono eu Eo LM PP .”
Sparse canonical methods for biological data integration: application to a cross-platform study
Cao, K. L., Martin, P. G. P., Robert-Granié, C., & Besse, P. C. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-34 · Cited by 284 · see citing works
Erratum: Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput
Gierahn, T. M., Wadsworth, M., Hughes, T., et al. (2017) · Nature Methods · doi:10.1038/nmeth0717-752a · Cited by 223
Self-citationSuper-resolution of human face image using canonical correlation analysis
Huang, H., He, H., Fan, X., & Zhang, J. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.02.007 · Cited by 212 · see citing works
Waaijenborg, S., Hamer, P. C. D. W., & Zwinderman, A. H. (2008) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1329 · Cited by 181 · see citing works
Endoplasmic Reticulum Stress, Pancreatic -Cell Degeneration, and DiabetesPreprint
Papa, F. R. (2012) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a007666 · Cited by 179 · see citing works
SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data
Welch, J. D., Hartemink, A. J., & Prins, J. F. (2016) · Genome biology · doi:10.1186/s13059-016-0975-3 · Cited by 153 · cited 2× · see citing works
“Joint identification of cell types across human and mouse islet scRNA-seq atlases (A-C) tSNE plots of 10,322 pancreatic islet cells from human (8,533) and mouse (1,789) donors, prior to (A) and post (B) alignment.”
BackgroundFast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts
Ntranos, V., Kamath, G. M., Zhang, J. M., Pachter, L. S., & Tse, D. N. C. (2016) · Genome biology · doi:10.1186/s13059-016-0970-8 · Cited by 107 · see citing works
(2016) · PubMed · Cited by 91 · see citing works
The Human Cell AtlasPreprint
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., Campbell, P. J., Carninci, P., Clatworthy, M. R., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/121202 · Cited by 76 · see citing works
Self-citationInduction Motor Fault Diagnosis Based on Deep Neural Network of Sparse Auto-encoder
Sun, W. (2016) · Journal of Mechanical Engineering · doi:10.3901/jme.2016.09.065 · Cited by 52 · see citing works
On multi-set canonical correlation analysis
Hasan, M. (2009) · 2009 International Joint Conference on Neural Networks · doi:10.1109/ijcnn.2009.5178958 · Cited by 29 · see citing works
Microbial Exposure Regulates the Development of Anti-Blood Group Antibodies
Arthur, C. M., Sullivan, H. C., Gerner‐Smidt, C., Kamili, N. A., Bennett, A. L., Patel, S. R., Henry, S., & Stowell, S. R. (2016) · Blood · doi:10.1182/blood.v128.22.20.20 · Cited by 17 · see citing works
Lacar, B., Linker, S. B., Jaeger, B. N., Krishnaswami, S. R., Barron, J. J., Kelder, M. J. E., Parylak, S., Paquola, A. C. M., Venepally, P., Novotny, M. A., et al. (2017) · Nature Communications · doi:10.1038/ncomms15047 · Cited by 8 · see citing works
Multiplexing droplet-based single cell RNA-sequencing using natural genetic barcodesPreprint
Kang, H., Subramaniam, M., Targ, S., et al. (2017) · bioRxiv · doi:10.1101/118778 · Cited by 8 · see citing works
Directing reconfigurable DNA nanoarrays
Yang, Y., & Lin, C.-X. (2017) · Science · doi:10.1126/science.aao0599 · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Single-cell reconstruction of the early maternal–fetal interface in humans
Vento-Tormo, R., Efremova, M., Botting, R. A., Turco, M. Y., Vento-Tormo, M., Meyer, K. B., Park, J.-E., Stephenson, E., Polański, K., Goncalves, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0698-6 · Cited by 2,358 · see citing works
SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data
Young, M. D., & Behjati, S. (2020) · GigaScience · doi:10.1093/gigascience/giaa151 · Cited by 1,619 · see citing works
“We include code to input the resulting cell specific expression profiles into popular downstream analysis packages (Seurat Satija et al., 2015; Butler and Satija, 2017 and SCANPY Wolf, Angerer, and Theis, 2018).”
MethodInfluentialStoeckius, M., Zheng, S., Houck-Loomis, B., Hao, S., Yeung, B. Z., Mauck, W. M., Smibert, P., & Satija, R. (2018) · Genome Biology · doi:10.1186/s13059-018-1603-1 · Cited by 1,046 · see citing works
“0, Satija Lab [29]) which enables the integrated processing of multi-modal (RNA, ADT, HTO) single cell datasets [31, 32].”
BackgroundSelf-citationDevelopmental diversification of cortical inhibitory interneurons
Mayer, C., Hafemeister, C., Bandler, R. C., Machold, R., Batista Brito, R., Jaglin, X., Allaway, K., Butler, A., Fishell, G., & Satija, R. (2018) · Nature · doi:10.1038/nature25999 · Cited by 492 · see citing works
Self-citationSkelly, D. A., Squiers, G. T., McLellan, M. A., Bolisetty, M. T., Robson, P., Rosenthal, N. A., & Pinto, A. R. (2018) · Cell Reports · doi:10.1016/j.celrep.2017.12.072 · Cited by 551 · see citing works
“To identify distinct cell populations based on shared and unique patterns of gene expression, we performed dimensionality reduction and unsupervised cell clustering using methods implemented in the Seurat software suite (Experimental Procedures; Butler and Satija, 2017; Macosko et al., 2015).”
InfluentialHair-bearing human skin generated entirely from pluripotent stem cells
Lee, J., Rabbani, C. C., Gao, H., Steinhart, M. R., Woodruff, B. M., Pflum, Z. E., Kim, A., Heller, S., Liu, Y., Shipchandler, T. Z., et al. (2020) · Nature · doi:10.1038/s41586-020-2352-3 · Cited by 487 · see citing works
Single-Cell Deconvolution of Fibroblast Heterogeneity in Mouse Pulmonary Fibrosis
Xie, T., Wang, Y., Deng, N., Huang, G., Taghavifar, F., Geng, Y., Liu, N., Kulur, V., Yao, C., Chen, P., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.010 · Cited by 528 · see citing works
“Statistical Method We used Cell Ranger version 1.3.1 (10x Genomics) to process raw sequencing data and Cell Ranger R kit version 2.0.0 and Seurat suite version 2.0.0 (Butler and Satija, 2017; Macosko et al., 2015) for downstream analysis.”
MethodInfluentialCrow, M., Paul, A., Ballouz, S., Huang, Z. J., & Gillis, J. (2018) · Nature Communications · doi:10.1038/s41467-018-03282-0 · Cited by 365 · see citing works
Single-Cell RNA Sequencing of Lymph Node Stromal Cells Reveals Niche-Associated Heterogeneity
Rodda, L. B., Lu, E., Bennett, M. L., Sokol, C. L., Wang, X., Luther, S. A., Barres, B. A., Luster, A. D., Ye, C., & Cyster, J. G. (2018) · Immunity · doi:10.1016/j.immuni.2018.04.006 · Cited by 441 · see citing works
“8%of cells from the uninfected and post-infection samples, respectively (Butler and Satija, 2017).”
InfluentialPaired-cell sequencing enables spatial gene expression mapping of liver endothelial cells
Halpern, K. B., Shenhav, R., Massalha, H., Toth, B., Egozi, A., Massasa, E. E., Medgalia, C., David, E., Giladi, A., Moor, A. E., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4231 · Cited by 368 · see citing works
Wu, H., Malone, A. F., Donnelly, E. L., Kirita, Y., Uchimura, K., Ramakrishnan, S. M., Gaut, J. P., & Humphreys, B. D. (2018) · Journal of the American Society of Nephrology · doi:10.1681/asn.2018020125 · Cited by 418 · see citing works
zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs
Parekh, S., Ziegenhain, C., Vieth, B., Enard, W., & Hellmann, I. (2018) · GigaScience · doi:10.1093/gigascience/giy059 · Cited by 349 · see citing works
“After processing the DroNc-seq data [12] with zUMIs as described above, we cluster cells based on UMI counts derived from Exons only and Exons+Introns reads using the Seurat pipeline [35, 36].”
Intratumoral heterogeneity and clonal evolution in liver cancer
Losic, B., Craig, A. J., Villacorta-Martín, C., Martins-Filho, S. N., Akers, N. K., Chen, X., Ahsen, M. E., von Felden, J., Labgaa, I., D’Avola, D., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-14050-z · Cited by 364 · see citing works
Exploring Single-Cell Data with Deep Multitasking Neural Networks
Amodio, M., van Dijk, D., Srinivasan, K., Chen, W. S., Mohsen, H., Moon, K. R., Campbell, A., Zhao, Y.-J., Wang, X.-M., Venkataswamy, M., et al. (2017) · Nature Methods · doi:10.1038/s41592-019-0576-7 · Cited by 288 · see citing works
“In order to do this, we compare SAUCIE to two published batch correction methods that have been specifically designed to remove batch effects in single-cell data, namely Mutual Nearest Neighbors (MNN) [23], which uses mutual nearest neighbors on a k-nearest neighbors graph to align two datasets, and Canonical Correlat…”
MethodPapalexi, E., Mimitou, E. P., Butler, A. W., Foster, S., Bracken, B., Mauck, W. M., Wessels, H.-H., Hao, Y., Yeung, B. Z., Smibert, P., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00778-2 · Cited by 238 · see citing works
Self-citationAn Immunocompetent Mouse Model of Zika Virus Infection
Gorman, M. J., Caine, E. A., Zaitsev, K., Begley, M., Weger‐Lucarelli, J., Uccellini, M. B., Tripathi, S., Morrison, J. M., Yount, B. L., Dinnon, K. H., et al. (2018) · Cell Host & Microbe · doi:10.1016/j.chom.2018.04.003 · Cited by 240 · see citing works
“0 Seurat R package Butler and Satija, 2017 https://github.com/satijalab/ seurat/; Version 2.1 Cell Ranger Single Cell Software Suite 10x Genomics https://github.com/10XGenomics/ cellranger/; Version 2.0.2 MAST R package Finak et al., 2015 https://github.com/RGLab/MAST; Version 1.4.0”
BackgroundMethodInfluentialKiss, T., Nyúl-Tóth, Á., Balasubramanian, P., Tarantini, S., Ahire, C., DelFavero, J., Yabluchanskiy, A., Csipo, T., Farkas, E., Wiley, G., et al. (2020) · GeroScience · doi:10.1007/s11357-020-00177-1 · Cited by 203 · see citing works
Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications
Van den Berge, K., Perraudeau, F., Soneson, C., Love, M. I., Risso, D., Vert, J.-P., Robinson, M. D., Dudoit, S., & Clement, L. (2018) · Genome Biology · doi:10.1186/s13059-018-1406-4 · Cited by 228 · see citing works
“Two different parameterizations were used for the SEURAT clustering.”
MethodInfluentialSingle-cell RNA-seq reveals hidden transcriptional variation in malaria parasites
Reid, A. J., Talman, A. M., Bennett, H. M., Gomes, A. R., Sanders, M. J., Illingworth, C. J. R., Billker, O., Berriman, M., & Lawniczak, M. (2018) · eLife · doi:10.7554/elife.33105 · Cited by 209 · see citing works
“The Plasmodium Drop-seq data (Poran et al., 2017) was loaded with the package Seurat version 2.1 (Butler and Satija, 2017), QC steps and normalisation were performed with the exact same parameters as in the released code (https://github.com/KafsackLab/scRNAseq-Malaria).”
MethodInfluentialSingle-Cell Genomics: A Stepping Stone for Future Immunology Discoveries
Giladi, A., & Amit, I. (2018) · Cell · doi:10.1016/j.cell.2017.11.011 · Cited by 168 · see citing works
Molecular transitions in early progenitors during human cord blood hematopoiesis
Zheng, S., Papalexi, E., Butler, A., Stephenson, W., & Satija, R. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178041 · Cited by 147 · see citing works
“To integrate this dataset with our Drop-seq micro-clusters, we ran the scRNA-seq integration procedure as described in Seurat 2.0 (Satija et al , 2015; preprint: Butler & Satija, 2017).”
InfluentialSelf-citationBenchmarking principal component analysis for large-scale single-cell RNA-sequencing
Tsuyuzaki, K., Sato, H., Sato, K., & Nikaido, I. (2020) · Genome biology · doi:10.1186/s13059-019-1900-3 · Cited by 123 · see citing works
“Cell Ranger [22], Seurat2 [47], Scanpy [87], SAFE [66], Scran [48], Giniclust2 [68], MAGIC [50], Harmony [55], and Scater [76] use IRLBA for PCA functions (Table 1).”
MethodInfluentialSperm is epigenetically programmed to regulate gene transcription in embryosPreprint
Teperek, M., Simeone, A., Gaggioli, V., Miyamoto, K., Allen, G. E., Erkek, S., Kwon, T., Marcotte, E. M., Zegerman, P., Bradshaw, C. R., et al. (2016) · Genome Research · doi:10.1101/gr.201541.115 · Cited by 128 · see citing works
Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data
Campbell, K. R., & Yau, C. (2018) · Nature Communications · doi:10.1038/s41467-018-04696-6 · Cited by 124 · see citing works
Glial cell diversity and methamphetamine-induced neuroinflammation in human cerebral organoids
Dang, J. W., Tiwari, S. K., Agrawal, K., Hui, H., Qin, Y., & Rana, T. M. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-0676-x · Cited by 123 · see citing works
“Integrative single-cell analyses were performed following previously established Seurat pipelines with modifications [25].”
MethodPower in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues
Birnbaum, K. D. (2018) · Annual Review of Genetics · doi:10.1146/annurev-genet-120417-031247 · Cited by 113 · see citing works
“Another approach works directly on gene–gene correlations across the two data sets, finding covarying genes across the two data sets to align the two batches using canonical correlation analysis (11).”
BackgroundMethodSingle-Cell Transcriptome Profiling of Mouse and hESC-Derived Pancreatic Progenitors
Krentz, N. A. J., Lee, M. Y. Y., Xu, E. E., Sproul, S. L. J., Maslova, A., Sasaki, S., & Lynn, F. C. (2018) · Stem Cell Reports · doi:10.1016/j.stemcr.2018.11.008 · Cited by 114 · see citing works
Stein-O’Brien, G., Clark, B. S., Sherman, T. D., Zibetti, C., Hu, Q., Sealfon, R. S. G., Liu, S., Qian, J. X., Colantuoni, C., Blackshaw, S., et al. (2019) · Cell Systems · doi:10.1016/j.cels.2019.04.004 · Cited by 166 · see citing works
“Moreover, these techniques have limited applicability for differences in data dimensionality and distributions (Butler and Satija, 2017; Wang et al., 2015).”
BackgroundStein-O'Brien, G., Clark, B. S., Sherman, T., et al. (2018) · bioRxiv · doi:10.1101/395004 · Cited by 80 · see citing works
“This is especially significant for integrating across molecular features where differences in data dimensionality and distribution currently limit implementation of techniques that enforce alignment to a mutually learned basis set (Butler and Satija, 2017; Wang et al., 2015).”
BackgroundD’Avola, D., Villacorta-Martín, C., Martins-Filho, S. N., Craig, A. J., Labgaa, I., von Felden, J., Allette, K., Bonaccorso, A., Tabrizian, P., Hartmann, B., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-30047-y · Cited by 84 · see citing works
Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists
Zhu, X., Wolfgruber, T., Tasato, A., Arisdakessian, C. G., Garmire, D. G., & Garmire, L. X. (2017) · Genome Medicine · doi:10.1186/s13073-017-0492-3 · Cited by 78 · see citing works
Zhang, S., Liu, Z., Wu, D., Chen, L., & Xie, L. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.596318 · Cited by 74 · see citing works
“1) (15) was performed to preprocess the collected data, genes expressed in fewer than three cells in a sample were excluded, as well as cells that expressed fewer than 200 genes or cells with mitochondrial gene content >10% of the total unique molecular identifier (UMI) count.”
MethodAlsema, A. M., Jiang, Q., Kracht, L., Gerrits, E., Dubbelaar, M. L., Miedema, A., Brouwer, N., Hol, E. M., Middeldorp, J., van Dijk, R. E., et al. (2020) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2020.00134 · Cited by 79 · see citing works
“In addition, expression of genes related to neurodegenerative disease from previous studies (Keren-Shaul et al., 2017; Friedman et al., 2018; Mathys et al., 2019) were not altered with meaningful effect sizes in any cluster.”
BackgroundMethodInfluentialStatistical single cell multi-omics integration
Colomé‐Tatché, M., & Theis, F. J. (2018) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2018.01.003 · Cited by 70 · see citing works
“Another approach is the one from Butler and Satija [39] , who have devised a strategy for performing integrated or comparative analysis of scRNA-seq datasets produced across different platforms, conditions or species.”
BackgroundSingle-cell genomics to guide human stem cell and tissue engineering
Camp, J. G., Wollny, D., & Treutlein, B. (2018) · Nature Methods · doi:10.1038/s41592-018-0113-0 · Cited by 83 · see citing works
Circadian control of interferon-sensitive gene expression in murine skin
Greenberg, E. N., Marshall, M. E., Jin, S., Venkatesh, S., Dragan, M., Tsoi, L. C., Gudjonsson, J. E., Nie, Q. H., Takahashi, J. S., & D, B. A. M. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1915773117 · Cited by 65 · see citing works
“The control and IMQ samples were analyzed separately as well as in a pooled fashion in order to perform integrative analysis using canonical correlation analysis (CCA) (48), as described in SI Appendix, Materials and Methods.”
Single Cell and Open Chromatin Analysis Reveals Molecular Origin of Epidermal Cells of the Skin
Fan, X., Wang, D., Burgmaier, J. E., Teng, Y., Romano, R., Sinha, S., & Yi, R. (2018) · Developmental Cell · doi:10.1016/j.devcel.2018.09.019 · Cited by 17 · see citing works
“We first focused on epithelial cells at E13 and used Seurat (Butler and Satija, 2017) to perform dimensionality reduction and generated t-SNE plot for the epithelial populations (Figure 4C and STAR Method).”
MethodInfluentialA single-cell transcriptomic atlas of the developing chicken limb
Feregrino, C., Sacher, F., Parnas, O., & Tschopp, P. (2019) · BMC Genomics · doi:10.1186/s12864-019-5802-2 · Cited by 77 · see citing works
Quantitative single-cell transcriptomics
Ziegenhain, C., Vieth, B., Parekh, S., Hellmann, I., & Enard, W. (2018) · Briefings in Functional Genomics · doi:10.1093/bfgp/ely009 · Cited by 58 · see citing works
“A possible approach is canonical correlation analysis to identify shared gene correlation structures across data sets to align cells for integrated comparative anlaysis [152], increasing the sample size and therefore power to detect biological signals in the inherent noisy and sparse scRNAseq data.”
BackgroundMapping Rora expression in resting and activated CD4+ T cells
Haim-Vilmovsky, L., Henriksson, J., Walker, J. A., Miao, Z., Natan, E., Kar, G., Clare, S., Barlow, J. L., Charidemou, E., Mamanova, L., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0251233 · Cited by 50 · see citing works
“Seurat [26] was used for single-cell analysis: Highly variable genes were calculated (x.”
MethodExpression reflects population structure
Brown, B. C., Bray, N., & Pachter, L. S. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007841 · Cited by 30 · see citing works
Singh, S. P., Janjuha, S., Chaudhuri, S., Reinhardt, S., Kränkel, A., Dietz, S., Eugster, A., Bilgin, H., Korkmaz, S., Zararsız, G., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-35218-5 · Cited by 28 · see citing works
Functional Genomics of the Pediatric Obese Asthma Phenotype Reveal Enrichment of Rho-GTPase Pathways
Rastogi, D., Johnston, A. D., Nico, J., Loh, L. N., Jorge, Y., Suzuki, M., Macián, F., & Greally, J. M. (2020) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201906-1199oc · Cited by 33 · see citing works
“Using the deconvolution approach in the Seurat toolkit (21), we analyzed 10x Genomics reference RNA-seq datasets on naive (CD4(1)CD45RA(1)CD25(2)), regulatory (CD4(1)CD25(1)), and memory (CD4(1)CD45RO(1)) Th cells and identified seven cell clusters (Figures E1A and E1B in the online supplement).”
MethodMethods and challenges in the analysis of single-cell RNA-sequencing data
Cámara, P. G. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.12.007 · Cited by 29 · see citing works
Végh, P., & Haniffa, M. A. (2018) · Briefings in Functional Genomics · doi:10.1093/bfgp/ely003 · Cited by 26 · see citing works
“Several toolkits facilitate scRNA-seq data analysis, such as Scater [48], Seurat [49] or Sincera [50]; however, the abovementioned software and statistical approaches are still currently under development and in the exploratory phase, in contrast to bulk RNA sequencing, where there is a consensus on analytical approac…”
Backgroundscmap - A tool for unsupervised projection of single cell RNA-seq dataPreprint
Kiselev, V., Yiu, A., & Hemberg, M. (2017) · bioRxiv · doi:10.1101/150292 · Cited by 23 · see citing works
Comparative transcriptomic analysis of hematopoietic system between human and mouse by Microwell-seq
Lai, S., Huang, W., Xu, Y., Jiang, M., Chen, H., Cheng, C., Lu, Y., Huang, H., Guo, G., & Han, X. (2018) · Cell Discovery · doi:10.1038/s41421-018-0038-x · Cited by 35 · see citing works
Stoeckius, M., Zheng, S.-W., Houck-Loomis, B., et al. (2017) · bioRxiv · doi:10.1101/237693 · Cited by 17 · see citing works
Self-citationscReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets
Liu, H., Prashant, N. M., Spurr, L. F., Bousounis, P., Alomran, N., Ibeawuchi, H., Sein, J., Słowiński, P., Tsaneva-Atanasova, K. T., & Horvath, A. (2021) · BMC Genomics · doi:10.1186/s12864-020-07334-y · Cited by 19 · see citing works
Single Cell and Open Chromatin Analysis Reveals Molecular Origin of Epidermal Cells of the Skin
Fan, X., Wang, D., Burgmaier, J. E., Teng, Y., Romano, R., Sinha, S., & Yi, R. (2018) · Developmental Cell · doi:10.1016/j.devcel.2018.08.010 · Cited by 68 · see citing works
Single cell immune profiling in transplantation research
Higdon, L. E., Schaffert, S. A., Khatri, P., & Maltzman, J. S. (2019) · American Journal of Transplantation · doi:10.1111/ajt.15316 · Cited by 10 · see citing works
CellexalVR: A virtual reality platform to visualise and analyse single-cell dataPreprint
Legetth, O., Rodhe, J., Lang, S., Dhapola, P., Pålsson, J., Wallergård, M., & Soneji, S. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/329102 · Cited by 19 · see citing works
Reconstructing the human first trimester fetal–maternal interface using single cell transcriptomicsPreprint
Vento-Tormo, R., Efremova, M., Botting, R. A., Turco, M. Y., Vento-Tormo, M., Meyer, K. B., Park, J.-E., Stephenson, E., Polański, K., Payne, R., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/429589 · Cited by 8 · see citing works
Soultoukis, G. A., Leer, M., Kehm, R., Villacorta, L., Beneš, V., Grune, T., Höhn, A., & Schulz, T. J. (2025) · Redox Biology · doi:10.1016/j.redox.2025.103791 · Cited by 9 · see citing works
“Moreover, scRNA-seq has some known methodological limitations, including low gene detection rate, sample processing and batch effects, and methodology-dependent biases including those stemming from computational analysis approaches, among others [39,40].”
Immunologically distinct responses occur in the CNS of COVID-19 patients
Song, E., Bartley, C. M., Chow, R. D., et al. (2020) · bioRxiv · doi:10.1101/2020.09.11.293464 · Cited by 8
Single-cell transcriptomic analysis of the adult mouse spinal cordPreprint
Blum, J. A., Klemm, S. L., Nakayama, L., Kathiria, A. S., Guttenplan, K. A., Hoang, P. T., Shadrach, J. L., Kaltschmidt, J. A., Greenleaf, W. J., & Gitler, A. D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.16.992958 · Cited by 8 · see citing works
Rora regulates activated T helper cells during inflammation
Haim-Vilmovsky, L., Henriksson, J., Walker, J. A., et al. (2019) · bioRxiv · doi:10.1101/709998 · Cited by 8
Ho, Y., Hu, P., Peel, M. T., Chen, S., Cámara, P. G., Epstein, D. J., Wu, H., & Liebhaber, S. A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/475558 · Cited by 8 · see citing works
Out-of-Sample Extrapolation with Neuron EditingPreprint
Amodio, M., van Dijk, D., Montgomery, R. R., Wolf, G., & Krishnaswamy, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.12198 · Cited by 9 · see citing works
“Addressing batch effects is a goal of many new models [28], [29], [30], [31], including some deep learning methods [32], [11].”
BackgroundSkelly, D. A., Squiers, G. T., McLellan, M. A., et al. (2017) · bioRxiv · doi:10.1101/201970 · Cited by 7
Philtjens, S., Turnbull, M. T., Thedy, B. P., Moon, Y., & Kim, J. (2020) · doi:10.1101/2020.04.27.064881 · Cited by 7 · see citing works
cellHarmony: Cell-level matching and holistic comparison of single-cell transcriptomesPreprint
DePasquale, E. A., Dexheimer, P., Schnell, D., Ferchen, K., Hay, S., Valiente-Alandí, Í., Blaxall, B. C., Grimes, H. L., & Salomonis, N. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/412080 · Cited by 7 · see citing works
“Although no clear differences could be identified from the different batches for the same donor, donor-skewed population predictions were evident using the software Seurat when blinded to donor composition ( Fig.”
MethodInfluentialAddressing the looming identity crisis in single cell RNA-seq
(2017) · doi:10.1101/150524 · Cited by 365 · see citing works
Xi, J., Park, S. R., Lee, J. H., & Kang, H. M. (2023) · Cell Systems · doi:10.1016/j.cels.2023.06.002 · Cited by 4 · see citing works
A single spectrum of neuronal identities across thalamusPreprint
Phillips, J. W., Schulmann, A., Hara, E., Liu, C., Wang, L., Shields, B. C., Korff, W. L., Lemire, A. L., Dudman, J. T., Nelson, S. B., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/241315 · Cited by 11 · see citing works
Jang, S., Gunmit, E., & Wichterle, H. (2022) · bioRxiv · doi:10.1101/2022.07.05.498885 · Cited by 3 · see citing works
Zhuang, J., Ren, C., Ren, D., Li, Y., Liu, D., Cui, L., Tian, G., Yang, J., & Liu, J. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.797057 · Cited by 3 · see citing works
“Seurat constructs weighted nearest neighbor graph based on typical correlation to obtain technology similarity between cells (9).”
BackgroundEdri, S., Hayward, P., Jawaid, W., et al. (2018) · bioRxiv · doi:10.1101/326371 · Cited by 2
“0 (Butler and Satija, 2017; Macosko et al., 2015) was used to read the data from Cell Ranger to R and build the expression matrix.”
MethodData Analysis in Single-Cell RNA-Seq
Zhu, X., & Garmire, L. X. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-814919-5.00019-1 · Cited by 1 · see citing works
Single-Cell, Human Sperm Transcriptomes and Variants from Fathers of Autistic and Healthy ChildrenPreprint
Tomoiaga, D., Aguiar‐Pulido, V., Shrestha, S., Feinstein, P. G., Levy, S., Rosenfeld, J., & Mason, C. E. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/640607
“Multiple pairwise alignment to remove batch effects and allow for integrated analysis was performed on all samples for each cohort as described and implemented in R toolkit Seurat (Seurat v.2.1.0 and 2.2.0)(Butler and Satija 2017).”
MethodInfluentialXu, R., Boreland, A. J., Li, X.-X., et al. (2019) · bioRxiv · doi:10.1101/594721 · Cited by 1 · see citing works
Observation weights to unlock bulk RNA-seq tools for zero inflation and single-cell applications
den Berge, K. V., Perraudeau, F., Soneson, C., et al. (2018) · bioRxiv · doi:10.1101/250126 · Cited by 1
“We then used W , instead of the first 10 components of PCA as in the Seurat tutorial, to cluster the cells using the Seurat graph-based clustering.”
MethodInfluentialCooper, R. A., Taylor, C. A., Watson, R. A., Tong, O., Nassiri, I., Sharma, P. K., Little, M., Ye, W., Koturan, S., Danielli, S., et al. (2024) · medRxiv · doi:10.1101/2024.01.25.24301653
Gillotay, P., Shankar, M., Haerlingen, B., et al. (2020) · bioRxiv · doi:10.1101/2020.01.13.891630
Comparison of clustering methods for single-cell RNA sequencing data
Duò, A. (2018) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-152434
“According to Butler and Satija (2017), the resolution parameter for the function is crucial to the ability to determine the number of clusters, and it is recommended that the method is tested using different values of this parameter.”
MethodThe cell cycle regulates mouse and human pancreas development
Krentz, N. A. J. (2017) · doi:10.14288/1.0362895
- Authors
- Butler, A., & Satija, R.
- Affiliations
- New York Genome Center (US)
- Corresponding author
- Satija, Rahul
- Venue
- Nature Biotechnology
- Publisher
- openRxiv
- Published
- 2017-07-18
- Volume
- 36
- Issue
- 5
- Pages
- 411-420
- ISSN
- 0733-222X (ISSN-L)
- PMC
- PMC6700744
- OpenAlex
- W2794480084
- Cited by
- 13,200
- References
- 53
- Citations by year
- 2026: 1305 · 2025: 1837 · 2024: 2122 · 2023: 1921 · 2022: 1786 · 2021: 1832 · 2020: 1417 · 2019: 760 · 2018: 205 · 2017: 5 · 2015: 1 · 2014: 1 · 2013: 1
- FWCI
- 380.84
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- 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
