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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]
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“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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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
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
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
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 3,231
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
(1994) · Knowledge Discovery and Data Mining · Cited by 3,137
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
Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
Dudoit, S., Fridlyand, J., & Speed, T. P. (2002) · Journal of the American Statistical Association · doi:10.1198/016214502753479248 · Cited by 2,671
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
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
Self-citationAccelerating t-SNE using tree-based algorithms
van der Maaten, L. J. P. (2014) · Journal of machine learning research · doi:10.5555/2627435.2697068 · Cited by 2,211
Ancient 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
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 1,756
Witten, D., Tibshirani, R. J., & Hastie, T. J. (2009) · Biostatistics · doi:10.1093/biostatistics/kxp008 · Cited by 1,549
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
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
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
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
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
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
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 1,323
Hastie, T. J., & Stuetzle, W. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478797 · Cited by 1,288
Computing and Visualizing Dynamic Time Warping Alignments inR: ThedtwPackage
Toni Giorgino, A. (2009) · Journal of Statistical Software · doi:10.18637/jss.v031.i07 · Cited by 1,263
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
Multiplexed droplet single-cell RNA-sequencing using natural genetic variation
Kang, H. M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., McCarthy, E., Wan, E., Wong, S., Byrnes, L., Lanata, C. M., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4042 · Cited by 1,101
A smart local moving algorithm for large-scale modularity-based community detection
Waltman, L., & van Eck, N. J. (2013) · The European Physical Journal B · doi:10.1140/epjb/e2013-40829-0 · Cited by 1,088
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
CD56 bright natural killer (NK) cells: an important NK cell subset
Poli, A., Michel, T., Thérésine, M., Andrès, E., Hentges, F., & Zimmer, J. (2008) · Immunology · doi:10.1111/j.1365-2567.2008.03027.x · Cited by 976
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
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
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
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
Canonical analysis of several sets of variables
Kettenring, J. R. (1971) · Biometrika · doi:10.1093/biomet/58.3.433 · Cited by 798
A 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
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 735
Visualization 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 699
Classification of low quality cells from single-cell RNA-seq data
Ilicic, T., Kim, J. K., Kolodziejczyk, A. A., Bagger, F. O., McCarthy, D. J., Marioni, J. C., & Teichmann, S. A. (2016) · Genome Biology · doi:10.1186/s13059-016-0888-1 · Cited by 693
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
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
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
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 553
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 543
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 541
Comprehensive literature review and statistical considerations for microarray meta-analysis
Tseng, G. C., Ghosh, D., & Feingold, E. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1265 · Cited by 532
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 515
Developmental 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
Self-citationDefining 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 484
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 425
Class Prediction by Nearest Shrunken Centroids, with Applications to DNA Microarrays
Tibshirani, R. J., Hastie, T. J., Narasimhan, B., & Chu, G. (2003) · Statistical Science · doi:10.1214/ss/1056397488 · Cited by 413
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
Augmented Implicitly Restarted Lanczos Bidiagonalization Methods
Baglama, J., & Reichel, L. (2005) · SIAM Journal on Scientific Computing · doi:10.1137/04060593x · Cited by 331
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
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
Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data
Nielsen, A. A. (2002) · IEEE Transactions on Image Processing · doi:10.1109/83.988962 · Cited by 300
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 297
Resolving early mesoderm diversification through single-cell expression profiling
Scialdone, A., Tanaka, Y., Jawaid, W., Moignard, V., Wilson, N. K., Macaulay, I. C., Marioni, J. C., & Göttgens, B. (2016) · Nature · doi:10.1038/nature18633 · Cited by 293 · cited 1×
“Therefore, independently for both data sets, we first assigned a cell cycle score to each cell using the PCA method 56 on a previously annotated list of cell cycle genes 57 .”
MethodSingle-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
Wu, H., Wang, Y., Gong, W., Han, Y., & Chen, X. H. (2012) · The European Physical Journal B · doi:10.1140/epjb/e2012-30793-6 · Cited by 241
Super-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
ATP sensing in living plant cells reveals tissue gradients and stress dynamics of energy physiology
Col, V. D., Fuchs, P., Nietzel, T., Elsässer, M., Voon, C. P., Candeo, A., Seeliger, I., Fricker, M. D., Grefen, C., Møller, I. M., et al. (2017) · eLife · doi:10.7554/elife.26770 · Cited by 207
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 184
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
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
Lember, J., & Koloydenko, A. A. (2014) · Journal of Machine Learning Research · Cited by 130
Band-edge diagrams for strained III–V semiconductor quantum wells, wires, and dots
Pryor, C., & Pistol, M. (2005) · Physical Review B · doi:10.1103/physrevb.72.205311 · Cited by 129
Fast 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 110
(2016) · PubMed · Cited by 91
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 86
Self-citationFace shape recovery from a single image using CCA mapping between tensor spaces
Lei, Z., Bai, Q., He, R., & Li, S. Z. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587341 · Cited by 71
Induction 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
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
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
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
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
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Self-citationIntegrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508
Self-citationFast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488
Dimensionality reduction for visualizing single-cell data using UMAP
Becht, E., McInnes, L., Healy, J., Dutertre, C.-A., Kwok, I. W. H., Ng, L. G., Ginhoux, F., & Newell, E. W. (2019) · Nature Biotechnology · doi:10.1038/nbt.4314 · Cited by 5,358
Hafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479
Self-citation
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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