Computational principles and challenges in single-cell data integration
Abstract
The development of single-cell multimodal assays provides a powerful tool for investigating multiple dimensions of cellular heterogeneity, enabling new insights into development, tissue homeostasis and disease. A key challenge in the analysis of single-cell multimodal data is to devise appropriate strategies for tying together data across different modalities. The term 'data integration' has been used to describe this task, encompassing a broad collection of approaches ranging from batch correction of individual omics datasets to association of chromatin accessibility and genetic variation with transcription. Although existing integration strategies exploit similar mathematical ideas, they typically have distinct goals and rely on different principles and assumptions. Consequently, new definitions and concepts are needed to contextualize existing methods and to enable development of new methods.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Holistic representations of cells can be obtained only with analyses across modalities, in which several modalities of the same cells are jointly examined.
“Holistic representations of cells can be obtained only with analyses across modalities245, whereby several modalities of the same cells are jointly examined.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [245]
A common task in single-cell analysis is to eliminate batch effects without removing meaningful biological differences, allowing for data integration.
“Hence, a common task in single-cell analysis is to eliminate batch effects without removing meaningful biological differences, allowing for data integration [13, 14, 15].”
From Assessing the limits of zero-shot foundation models in single-cell biology (2023) · cites this paper as [14]
Much of the work in single-cell biology is inherently exploratory, where labels may not be available a priori.
“However, much of the work in single-cell biology is inherently exploratory, where labels may not be available a priori .”
From Assessing the limits of zero-shot foundation models in single-cell biology (2023) · cites this paper as [14]
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
Integrated 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 · see citing works
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 · see citing works
Fast, 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 · 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
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
Yu, J., Pressoir, G., Briggs, W. H., Bi, I. V., Yamasaki, M., Doebley, J., McMullen, M. D., Gaut, B. S., Nielsen, D. M., Holland, J. B., et al. (2005) · Nature Genetics · doi:10.1038/ng1702 · Cited by 4,155 · see citing works
Genome-wide efficient mixed-model analysis for association studies
Zhou, X., & Stephens, M. (2012) · Nature Genetics · doi:10.1038/ng.2310 · Cited by 3,627 · cited 2× · see citing works
“Linear mixed models (LMMs) are a popular analytical framework to address this challenge 41–46 .”
BackgroundMethodSimultaneous epitope and transcriptome measurement in single cells
Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P. (2017) · Nature Methods · doi:10.1038/nmeth.4380 · Cited by 3,098 · see citing works
Molecular Architecture of the Mouse Nervous System
Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,995 · see citing works
Spatially resolved, highly multiplexed RNA profiling in single cells
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., & Zhuang, X. (2015) · Science · doi:10.1126/science.aaa6090 · Cited by 2,969 · see citing works
Deep generative modeling for single-cell transcriptomics
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018) · Nature Methods · doi:10.1038/s41592-018-0229-2 · Cited by 2,530 · see citing works
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416 · see citing works
Self-citationDixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L., Marjanovic, N. D., Dionne, D., Burks, T., Raychowdhury, R., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.038 · Cited by 1,863 · see citing works
Molecular diversity and specializations among the cells of the adult mouse brain
Saunders, A., Macosko, E. Z., Wysoker, A., Goldman, M., Krienen, F. M., de Rivera, H., Bien, E., Baum, M., Bortolin, L., Wang, S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.07.028 · Cited by 1,847 · see citing works
Applications of linear models in animal breeding
Henderson, C. R. (1984) · University of Guelph eBooks · Cited by 1,700 · see citing works
chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data
Schep, A. N., Wu, B., Buenrostro, J. D., & Greenleaf, W. J. (2017) · Nature Methods · doi:10.1038/nmeth.4401 · Cited by 1,680 · see citing works
A molecular cell atlas of the human lung from single-cell RNA sequencing
Travaglini, K. J., Nabhan, A. N., Penland, L., Sinha, R., Gillich, A., Sit, R. V., Chang, S., Conley, S. D., Mori, Y., Seita, J., et al. (2020) · Nature · doi:10.1038/s41586-020-2922-4 · Cited by 1,657 · see citing works
Three-dimensional intact-tissue sequencing of single-cell transcriptional states
Wang, X., Allen, W. E., Wright, M. A., Sylwestrak, E. L., Samusik, N., Vesuna, S., Evans, K., Liu, C., Ramakrishnan, C., Liu, J., et al. (2018) · Science · doi:10.1126/science.aat5691 · Cited by 1,620 · see citing works
Genetics of gene expression surveyed in maize, mouse and man
Schadt, E., Monks, S. A., Drake, T., Lusis, A., Che, N., Colinayo, V. V., Ruff, T., Milligan, S. B., Lamb, J., Cavet, G., et al. (2003) · Nature · doi:10.1038/nature01434 · Cited by 1,594 · see citing works
Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes
Segerstolpe, Å., Palasantza, A., Eliasson, P., Andersson, E.-M., Andréasson, A.-C., Sun, X., Picelli, S., Sabirsh, A., Clausen, M., Bjursell, M. K., et al. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.08.020 · Cited by 1,550 · see citing works
Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets
Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J. C., Buettner, F., Huber, W., & Stegle, O. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178124 · Cited by 1,510 · see citing works
Self-citationBaron, 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
Efficient Bayesian mixed-model analysis increases association power in large cohorts
Loh, P.-R., Tucker, G., Bulik-Sullivan, B. K., Vilhjálmsson, B. J., Finucane, H. K., Salem, R. M., Chasman, D. I., Ridker, P. M., Neale, B. M., Berger, B., et al. (2015) · Nature Genetics · doi:10.1038/ng.3190 · Cited by 1,507 · see citing works
Robust decomposition of cell type mixtures in spatial transcriptomics
Cable, D. M., Murray, E., Zou, L. S., Goeva, A., Macosko, E. Z., Chen, F., & Irizarry, R. A. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-00830-w · Cited by 1,487 · see citing works
Integrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460 · 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
Benchmarking atlas-level data integration in single-cell genomics
Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01336-8 · Cited by 1,438 · see citing works
Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
Rosenberg, A. B., Roco, C. M., Muscat, R. A., Kuchina, A., Sample, P., Yao, Z., Graybuck, L. T., Peeler, D. J., Mukherjee, S., Chen, W., et al. (2018) · Science · doi:10.1126/science.aam8999 · Cited by 1,422 · 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 · see citing works
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 1,339 · see citing works
Eleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327 · see citing works
Self-citationA Single-Cell Transcriptome Atlas of the Human Pancreas
Muraro, M. J., Dharmadhikari, G., Grün, D., Gröen, N., Dielen, T., Jansen, E., van Gurp, L., Engelse, M. A., Carlotti, F., de Koning, E. J. P., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.09.002 · Cited by 1,288 · see citing works
FaST linear mixed models for genome-wide association studies
Lippert, C., Listgarten, J., Liu, Y., Kadie, C. M., Davidson, R. I., & Heckerman, D. (2011) · Nature Methods · doi:10.1038/nmeth.1681 · Cited by 1,209 · see citing works
Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity
Welch, J. D., Kozareva, V., Ferreira, A., Vanderburg, C., Martin, C., & Macosko, E. Z. (2019) · Cell · doi:10.1016/j.cell.2019.05.006 · Cited by 1,193 · see citing works
New approaches to population stratification in genome-wide association studies
Price, A. L., Zaitlen, N., Reich, D., & Patterson, N. J. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2813 · Cited by 1,181 · see citing works
Computational and analytical challenges in single-cell transcriptomics
Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170 · see citing works
Self-citationChromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin
Ma, S., Zhang, B., LaFave, L. M., Earl, A. S., Chiang, Z., Hu, Y., Ding, J., Brack, A., Kartha, V. K., Tay, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.056 · Cited by 1,146 · see citing works
Buettner, F., Natarajan, K. N., Casale, F. P., Proserpio, V., Scialdone, A., Theis, F. J., Teichmann, S. A., Marioni, J. C., & Stegle, O. (2015) · Nature Biotechnology · doi:10.1038/nbt.3102 · Cited by 1,136 · see citing works
Self-citationPooled CRISPR screening with single-cell transcriptome readout.
Datlinger, P., Rendeiro, A. F., Schmidl, C., Krausgruber, T., Traxler, P., Klughammer, J., Schuster, L. C., Kuchler, A., Alpar, D., & Bock, C. (2017) · Nature Methods · doi:10.1038/nmeth.4177 · Cited by 1,096 · cited 1× · see citing works
“Similar methods can be applied to test for regulatory effects of genetic perturbations (rather than natural variation) on different molecular readouts, through the use of assays such as Perturb-seq 58,59 ,CROP-seq 60 and TAP-seq 61 .”
MethodMOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data
Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Stegle, O. (2020) · Genome Biology · doi:10.1186/s13059-020-02015-1 · Cited by 1,091 · see citing works
Self-citationDIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays
Singh, A., Shannon, C. P., Gautier, B., Rohart, F., Vacher, M. A., Tebbutt, S. J., & Cao, K. L. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty1054 · Cited by 1,076 · see citing works
Stegle, O., Parts, L., Piipari, M., Winn, J., & Durbin, R. (2012) · Nature Protocols · doi:10.1038/nprot.2011.457 · Cited by 1,049 · see citing works
Self-citationA single-cell molecular map of mouse gastrulation and early organogenesis
Pijuan-Sala, B., Griffiths, J. A., Guibentif, C., Hiscock, T. W., Jawaid, W., Calero-Nieto, F. J., Mulas, C., Ibarra-Soria, X., Tyser, R. C. V., Ho, D. L. L., et al. (2019) · Nature · doi:10.1038/s41586-019-0933-9 · Cited by 1,033 · see citing works
Self-citationJoint profiling of chromatin accessibility and gene expression in thousands of single cells
Cao, J., Cusanovich, D. A., Ramani, V., Aghamirzaie, D., Pliner, H. A., Hill, A. J., Daza, R. M., McFaline-Figueroa, J. L., Packer, J. S., Christiansen, L., et al. (2018) · Science · doi:10.1126/science.aau0730 · Cited by 938 · see citing works
Exponential scaling of single-cell RNA-seq in the past decade
Svensson, V., Vento‐Tormo, R., & Teichmann, S. A. (2018) · Nature Protocols · doi:10.1038/nprot.2017.149 · Cited by 936 · see citing works
BBKNN: fast batch alignment of single cell transcriptomes
Polański, K., Young, M. D., Miao, Z., Meyer, K. B., Teichmann, S. A., & Park, J.-E. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz625 · Cited by 932 · see citing works
A cellular census of human lungs identifies novel cell states in health and in asthma
Vieira Braga, F. A., Kar, G., Berg, M., Carpaij, O. A., Polanski, K., Simon, L. M., Brouwer, S., Gomes, T., Hesse, L., Jiang, J., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0468-5 · Cited by 896 · see citing works
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
Hie, B., Bryson, B. D., & Berger, B. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0113-3 · Cited by 853 · see citing works
High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell
Chen, S., Lake, B. B., & Zhang, K. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0290-0 · Cited by 810 · see citing works
The origin and evolution of cell types
Arendt, D., Musser, J. M., Baker, C. V. H., Bergman, A., Cepko, C., Erwin, D. H., Pavlicev, M., Schlosser, G., Widder, S., Laubichler, M. D., et al. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.127 · Cited by 764 · see citing works
A human cell atlas of fetal gene expression
Cao, J., O’Day, D. R., Pliner, H. A., Kingsley, P. D., Deng, M., Daza, R. M., Zager, M. A., Aldinger, K. A., Blecher-Gonen, R., Zhang, F., et al. (2020) · Science · doi:10.1126/science.aba7721 · Cited by 764 · see citing works
Organoid single-cell genomic atlas uncovers human-specific features of brain development
Kanton, S., Boyle, M. J., He, Z., Santel, M., Weigert, A., Sanchís-Calleja, F., Guijarro, P., Sidow, L., Fleck, J. S., Han, D., et al. (2019) · Nature · doi:10.1038/s41586-019-1654-9 · Cited by 761 · see citing works
scmap: projection of single-cell RNA-seq data across data sets
(2018) · doi:10.1038/nmeth.4644 · Cited by 735 · see citing works
Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I., & Heyn, H. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab043 · Cited by 732 · see citing works
Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity
Angermueller, C., Clark, S. J., Lee, H. J., Macaulay, I. C., Teng, M. J., Hu, T. X., Krueger, F., Smallwood, S. A., Ponting, C. P., Voet, T., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3728 · Cited by 717 · see citing works
Self-citationSpatialDE: identification of spatially variable genes
Svensson, V., Teichmann, S. A., & Stegle, O. (2018) · Nature Methods · doi:10.1038/nmeth.4636 · Cited by 695 · see citing works
Self-citationClark, S. J., Argelaguet, R., Kapourani, C.-A., Stubbs, T. M., Lee, H. J., Alda-Catalinas, C., Krueger, F., Sanguinetti, G., Kelsey, G., Marioni, J. C., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03149-4 · Cited by 657 · see citing works
Self-citationFunctional and evolutionary implications of gene orthology
Gabaldón, T., & Koonin, E. V. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3456 · Cited by 643 · see citing works
Transfer Learning in Natural Language Processing
Ruder, S., Peters, M. E., Swayamdipta, S., & Wolf, T. (2019) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n19-5004 · Cited by 636 · see citing works
Missing data and technical variability in single-cell RNA-sequencing experiments
Hicks, S. C., Townes, F. W., Teng, M., & Irizarry, R. A. (2018) · Biostatistics · doi:10.1093/biostatistics/kxx053 · Cited by 635 · see citing works
A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens
Gasperini, M., Hill, A. J., McFaline-Figueroa, J. L., Martin, B., Kim, S., Zhang, M. D., Jackson, D., Leith, A., Schreiber, J., Noble, W. S., et al. (2019) · Cell · doi:10.1016/j.cell.2018.11.029 · Cited by 613 · see citing works
Joint and individual variation explained (JIVE) for integrated analysis of multiple data types
Lock, E. F., Hoadley, K. A., Marron, J. S., & Nobel, A. B. (2013) · The Annals of Applied Statistics · doi:10.1214/12-aoas597 · Cited by 554 · see citing works
Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex
Luo, C., Keown, C. L., Kurihara, L., Zhou, J., He, Y., Li, J., Castanon, R., Lucero, J., Nery, J. R., Sandoval, J. P., et al. (2017) · Science · doi:10.1126/science.aan3351 · Cited by 545 · see citing works
Lawlor, N. T., George, J., Bolisetty, M. T., Kursawe, R., Sun, L., Vijayakumar, S., Kycia, I., Robson, P., & Stitzel, M. L. (2016) · Genome Research · doi:10.1101/gr.212720.116 · Cited by 540 · see citing works
Mimitou, E. P., Cheng, A., Montalbano, A., Hao, S., Stoeckius, M., Legut, M., Roush, T., Herrera, A., Papalexi, E., Ouyang, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0392-0 · Cited by 520 · see citing works
Expression quantitative trait loci: present and future
Nica, A. C., & Dermitzakis, E. T. (2013) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2012.0362 · Cited by 498 · see citing works
Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia
Granja, J. M., Klemm, S., McGinnis, L. M., Kathiria, A. S., Mezger, A., Corces, M. R., Parks, B., Gars, E., Liedtke, M., Zheng, G. X. Y., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0332-7 · Cited by 498 · see citing works
Fairfax, B. P., Makino, S., Radhakrishnan, J., Plant, K., Leslie, S., Dilthey, A., Ellis, P., Langford, C., Vannberg, F. O., & Knight, J. C. (2012) · Nature Genetics · doi:10.1038/ng.2205 · Cited by 486 · see citing works
Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography
Andersson, A., Bergenstråhle, J., Asp, M., Bergenstråhle, L., Jurek, A., Navarro, J. F., & Lundeberg, J. (2020) · Communications Biology · doi:10.1038/s42003-020-01247-y · Cited by 463 · see citing works
Single-cell multiomics: multiple measurements from single cells
Macaulay, I. C., Ponting, C. P., & Voet, T. (2017) · Trends in Genetics · doi:10.1016/j.tig.2016.12.003 · Cited by 451 · see citing works
Multi-omics profiling of mouse gastrulation at single-cell resolution
Argelaguet, R., Clark, S. J., Mohammed, H., Stapel, L. C., Krueger, C., Kapourani, C.-A., Imaz-Rosshandler, I., Lohoff, T., Xiang, Y., Hanna, C. W., et al. (2019) · Nature · doi:10.1038/s41586-019-1825-8 · Cited by 450 · see citing works
Self-citationSpeech discrimination by dynamic programming
Vintsyuk, T. K. (1972) · Cybernetics and Systems Analysis · doi:10.1007/bf01074755 · Cited by 439 · see citing works
Statistical challenges of high-dimensional data
Johnstone, I. M., & Titterington, D. M. (2009) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2009.0159 · Cited by 420 · see citing works
Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs
Study, L. C., van der Wijst, M. G. P., BIOS Consortium, Brugge, H., de Vries, D. H., Deelen, P., Swertz, M. A., & Franke, L. (2018) · Nature Genetics · doi:10.1038/s41588-018-0089-9 · Cited by 399 · see citing works
Dimension reduction techniques for the integrative analysis of multi-omics data
Meng, C., Zeleznik, O. A., Thallinger, G., Küster, B., Gholami, A. M., & Culhane, A. C. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbv108 · Cited by 383 · see citing works
A human cell atlas of fetal chromatin accessibility
Domcke, S., Hill, A. J., Daza, R. M., Cao, J., O’Day, D. R., Pliner, H. A., Aldinger, K. A., Pokholok, D., Zhang, F., Milbank, J. H., et al. (2020) · Science · doi:10.1126/science.aba7612 · Cited by 375 · see citing works
Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells
Guo, F., Li, L., Li, J., Wu, X., Hu, B., Zhu, P., Wen, L., & Tang, F. (2017) · Cell Research · doi:10.1038/cr.2017.82 · Cited by 374 · see citing works
The first five years of single-cell cancer genomics and beyondPreprint
Navin, N. E. (2015) · Genome Research · doi:10.1101/gr.191098.115 · Cited by 368 · see citing works
Simultaneous profiling of 3D genome structure and DNA methylation in single human cells
Lee, D.-S., Luo, C., Zhou, J., Chandran, S., Rivkin, A., Bartlett, A., Nery, J. R., Fitzpatrick, C., O’Connor, C., Dixon, J. R., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0547-z · Cited by 340 · see citing works
Preissl, S., Fang, R., Huang, H., Zhao, Y., Raviram, R., Gorkin, D. U., Zhang, Y., Sos, B. C., Afzal, V., Dickel, D. E., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0079-3 · Cited by 332 · see citing works
Cuomo, A. S. E., Seaton, D. D., McCarthy, D. J., Martinez, I., Bonder, M. J., Garcia-Bernardo, J., Amatya, S., Madrigal, P., Isaacson, A., Buettner, F., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14457-z · Cited by 331 · see citing works
Self-citationA multivariate approach to the integration of multi-omics datasets
Meng, C., Küster, B., Culhane, A. C., & Gholami, A. M. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-162 · Cited by 319 · see citing works
Rubin, A. J., Parker, K. R., Satpathy, A. T., Qi, Y., Wu, B., Ong, A. J., Mumbach, M. R., Ji, A. L., Kim, D. S., Cho, S. W., et al. (2019) · Cell · doi:10.1016/j.cell.2018.11.022 · Cited by 314 · see citing works
BASiCS: Bayesian Analysis of Single-Cell Sequencing Data
Vallejos, C. A., Marioni, J. C., & Richardson, S. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004333 · Cited by 302 · see citing works
Self-citationSimultaneous profiling of transcriptome and DNA methylome from a single cell
Hu, Y., Huang, K., An, Q., Du, G., Hu, G., Xue, J., Zhu, X., Wang, C.-Y., Xue, Z., & Fan, G. (2016) · Genome Biology · doi:10.1186/s13059-016-0950-z · Cited by 300 · see citing works
Joint analysis of heterogeneous single-cell RNA-seq dataset collections
Barkas, N., Petukhov, V., Nikolaeva, D., Lozinsky, Y., Demharter, S., Khodosevich, K., & Kharchenko, P. V. (2019) · Nature Methods · doi:10.1038/s41592-019-0466-z · Cited by 292 · see citing works
Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation
Jerber, J., Seaton, D. D., Cuomo, A. S. E., Kumasaka, N., Haldane, J., Steer, J., Patel, M. J., Pearce, D., Andersson, M. H. L., Bonder, M. J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00801-6 · Cited by 288 · see citing works
Self-citationIntegrative Methods and Practical Challenges for Single-Cell Multi-omics
Ma, A., McDermaid, A., Xu, J., Chang, Y., & Ma, Q. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.02.013 · Cited by 272 · see citing works
Targeted Perturb-seq enables genome-scale genetic screens in single cells
Schraivogel, D., Gschwind, A. R., Milbank, J. H., Leonce, D. R., Jakob, P., Mathur, L., Korbel, J. O., Merten, C. A., Velten, L., & Steinmetz, L. M. (2020) · Nature Methods · doi:10.1038/s41592-020-0837-5 · Cited by 245 · see citing works
Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments
Wills, Q. F., Livak, K. J., Tipping, A. J., Enver, T., Goldson, A. J., Sexton, D. W., & Holmes, C. C. (2013) · Nature Biotechnology · doi:10.1038/nbt.2642 · Cited by 244 · see citing works
Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity
Liu, L., Liu, C., Quintero, A., Wu, L., Yuan, Y., Wang, M., Cheng, M., Leng, L., Xu, L., Dong, G., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08205-7 · Cited by 243 · see citing works
Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer
Cantini, L., Zakeri, P., Hernandez, C., Naldi, A., Thieffry, D., Rémy, É., & Baudot, A. (2021) · Nature Communications · doi:10.1038/s41467-020-20430-7 · Cited by 230 · see citing works
Dynamic genetic regulation of gene expression during cellular differentiation
Strober, B. J., Elorbany, R., Rhodes, K., Krishnan, N., Tayeb, K., Battle, A., & Gilad, Y. (2019) · Science · doi:10.1126/science.aaw0040 · Cited by 219 · see citing works
Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis
Arnol, D., Schapiro, D., Bodenmiller, B., Sáez-Rodríguez, J., & Stegle, O. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.08.077 · Cited by 218 · see citing works
Self-citationSpatially resolved transcriptome profiling in model plant species
Giacomello, S., Salmén, F., Terebieniec, B. K., Vicković, S., Navarro, J. F., Alexeyenko, A., Reimegård, J., McKee, L. S., Mannapperuma, C., Bulone, V., et al. (2017) · Nature Plants · doi:10.1038/nplants.2017.61 · Cited by 217 · see citing works
A molecular cell atlas of the human lung from single cell RNA sequencingPreprint
Travaglini, K. J., Nabhan, A. N., Penland, L., Sinha, R., Gillich, A., Sit, R. V., Chang, S., Conley, S. D., Mori, Y., Seita, J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/742320 · Cited by 216 · see citing works
The single-cell eQTLGen consortium
van der Wijst, M. G. P., de Vries, D., Groot, H. E., Trynka, G., Hon, C.-C., Bonder, M. J., Stegle, O., Nawijn, M. C., Idaghdour, Y., van der Harst, P., et al. (2020) · eLife · doi:10.7554/elife.52155 · Cited by 215 · see citing works
Self-citationCross-Domain Fault Diagnosis Using Knowledge Transfer Strategy: A Review
Zheng, H., Wang, R., Yang, Y., Yin, J., Li, Y., Li, Y., & Xu, M. (2019) · IEEE Access · doi:10.1109/access.2019.2939876 · Cited by 209 · see citing works
Single-Cell (Multi)omics Technologies
Chappell, L., Russell, A. J. C., & Voet, T. (2018) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-091416-035324 · Cited by 206 · see citing works
Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations
Duren, Z., Chen, X. X., Zamanighomi, M., Zeng, W., Satpathy, A. T., Chang, H. Y., Wang, Y., & Wong, W. H. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1805681115 · Cited by 201 · see citing works
Wang, A., Chiou, J., Poirion, O., Buchanan, J., Valdez, M. J., Verheyden, J. M., Hou, X., Kudtarkar, P., Narendra, S., Newsome, J. M., et al. (2020) · eLife · doi:10.7554/elife.62522 · Cited by 201 · see citing works
Single-cell multimodal profiling reveals cellular epigenetic heterogeneity
Cheow, L. F., Courtois, E. T., Tan, Y., Viswanathan, R., Xing, Q. R., Tan, R. Z., Tan, D. S.-W., Robson, P., Loh, Y., Quake, S. R., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3961 · Cited by 193 · see citing works
Data denoising with transfer learning in single-cell transcriptomics
Wang, J., Agarwal, D., Huang, M. C., Hu, G., Zhou, Z., Ye, C., & Zhang, N. R. (2019) · Nature Methods · doi:10.1038/s41592-019-0537-1 · Cited by 190 · see citing works
Integrative analyses of single-cell transcriptome and regulome using MAESTRO
Wang, C., Sun, D., Huang, X., Wan, C., Li, Z., Han, Y., Qin, Q., Fan, J., Qiu, X., Xie, Y., et al. (2020) · Genome biology · doi:10.1186/s13059-020-02116-x · Cited by 183 · see citing works
Species identity of insect cell lines
Greene, A. E., Charney, J., Nichols, W. W., & Coriell, L. L. (1972) · In Vitro Cellular & Developmental Biology - Plant · doi:10.1007/bf02661721 · Cited by 178 · see citing works
Fast, sensitive, and accurate integration of single cell data with HarmonyPreprint
Korsunsky, I., Fan, J., Slowikowski, K., et al. (2018) · bioRxiv · doi:10.1101/461954 · Cited by 177 · see citing works
Jin, S., Zhang, L., & Nie, Q. H. (2020) · Genome biology · doi:10.1186/s13059-020-1932-8 · Cited by 174 · see citing works
Single nucleus multi-omics identifies human cortical cell regulatory genome diversity
Luo, C., Liu, H., Xie, F., Armand, E. J., Siletti, K., Bakken, T. E., Fang, R., Doyle, W. I., Stuart, T., Hodge, R. D., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100107 · Cited by 167 · see citing works
From genome to function by studying eQTLs
Westra, H.-J., & Franke, L. (2014) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2014.04.024 · Cited by 166 · see citing works
Exponential scaling of single-cell RNA-seq in the last decadePreprint
Svensson, V., Tormo, R. V., & Teichmann, S. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01379 · Cited by 166 · see citing works
Single-cell analyses to tailor treatments
Shalek, A. K., & Benson, M. (2017) · Science Translational Medicine · doi:10.1126/scitranslmed.aan4730 · Cited by 157 · see citing works
LifeTime and improving European healthcare through cell-based interceptive medicine
Rajewsky, N., Almouzni, G., Gorski, S. A., Aerts, S., Amit, I., Bertero, M. G., Bock, C., Bredenoord, A. L., Cavalli, G., Chiocca, S., et al. (2020) · Nature · doi:10.1038/s41586-020-2715-9 · Cited by 156 · see citing works
Self-citationA Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens
Gasperini, M. J., Hill, A. J., McFaline‐Figueroa, J. L., Martin, B., Kim, S., Zhang, M. D., Jackson, D., Leith, A., Schreiber, J. M., Noble, W. S., et al. (2019) · Cell · doi:10.1016/j.cell.2019.02.027 · Cited by 150 · see citing works
Welch, J. D., Hartemink, A. J., & Prins, J. F. (2017) · Genome Biology · doi:10.1186/s13059-017-1269-0 · Cited by 140 · see citing works
Unsupervised topological alignment for single-cell multi-omics integration
Cao, K., Bai, X., Hong, Y., & Wan, L. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa443 · Cited by 130 · see citing works
f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq
Buettner, F., Pratanwanich, P. N., McCarthy, D. J., Marioni, J. C., & Stegle, O. (2017) · Genome biology · doi:10.1186/s13059-017-1334-8 · Cited by 128 · see citing works
Self-citationChromatin potential identified by shared single cell profiling of RNA and chromatinPreprint
Ma, S., Zhang, B. M., LaFave, L. M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.17.156943 · Cited by 125 · see citing works
Benchmarking atlas-level data integration in single-cell genomicsPreprint
Luecken, M. D., Büttner, M., Chaichoompu, K., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.22.111161 · Cited by 122 · see citing works
Toward a Common Coordinate Framework for the Human Body
Rood, J., Stuart, T., Ghazanfar, S., Biancalani, T., Fisher, E., Butler, A., Hupalowska, A., Gaffney, L., Mauck, W. M., Eraslan, G., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.019 · Cited by 115 · see citing works
Self-citationLieberman, Y., Rokach, L., & Shay, T. (2018) · PLoS ONE · doi:10.1371/journal.pone.0205499 · Cited by 113 · see citing works
Arto Klami, A., Virtanen, S., Leppäaho, E., & Samuel Kaski, A. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2376974 · Cited by 105 · see citing works
Profiling the transcriptome with RNA SPOTs
Eng, C.-H. L., Shah, S., Thomassie, J., & Cai, L. (2017) · Nature Methods · doi:10.1038/nmeth.4500 · Cited by 105 · see citing works
How Single-Cell Genomics Is Changing Evolutionary and Developmental Biology
Marioni, J. C., & Arendt, D. (2017) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-100616-060818 · Cited by 104 · see citing works
Self-citationCross-Species Analysis of Single-Cell Transcriptomic Data
Shafer, M. E. R. (2019) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2019.00175 · Cited by 103 · see citing works
scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data
Johansen, N., & Quon, G. (2019) · Genome biology · doi:10.1186/s13059-019-1766-4 · Cited by 101 · see citing works
Alignment of single-cell trajectories to compare cellular expression dynamics
Alpert, A., Moore, L. S., Dubovik, T., & Shen-Orr, S. S. (2018) · Nature Methods · doi:10.1038/nmeth.4628 · Cited by 96 · see citing works
Bravo González‐Blas, C., Quan, X., Duran‐Romaña, R., Taskiran, I. I., Koldere, D., Davie, K., Christiaens, V., Makhzami, S., Hulselmans, G., de Waegeneer, M., et al. (2020) · Molecular Systems Biology · doi:10.15252/msb.20209438 · Cited by 88 · see citing works
Kleshchevnikov, V., Shmatko, A., Dann, E., Aivazidis, A., King, H. W., Li, T., Lomakin, A., Kedlian, V. R., Jain, M. S., Park, J. S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.15.378125 · Cited by 88 · see citing works
Self-citationSingle-Cell Multi-omics: An Engine for New Quantitative Models of Gene Regulation
Packer, J. S., & Trapnell, C. (2018) · Trends in Genetics · doi:10.1016/j.tig.2018.06.001 · Cited by 84 · see citing works
Mapping human cell phenotypes to genotypes with single-cell genomics
Camp, J. G., Platt, R. J., & Treutlein, B. (2019) · Science · doi:10.1126/science.aax6648 · Cited by 83 · see citing works
Cacchiarelli, D., Qiu, X., Srivatsan, S. R., Manfredi, A., Ziller, M. J., Overbey, E. G., Grimaldi, A., Grimsby, J., Pokharel, P., Livak, K. J., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2018.07.006 · Cited by 77 · see citing works
Jointly embedding multiple single-cell omics measurementsPreprint
Liu, J., Huang, Y., Singh, R., Vert, J., & Noble, W. S. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/644310 · Cited by 73 · see citing works
Discovery and characterization of variance QTLs in human induced pluripotent stem cells
Sarkar, A. K., Tung, P.-Y., Blischak, J. D., Burnett, J. E., Li, Y. I., Stephens, M., & Gilad, Y. (2019) · PLOS Genetics · doi:10.1371/journal.pgen.1008045 · Cited by 72 · see citing works
Pijuan-Sala, B., Wilson, N. K., Xia, J., Hou, X., Hannah, R., Kinston, S., Calero‐Nieto, F. J., Poirion, O., Preißl, S., Liu, F., et al. (2020) · Nature Cell Biology · doi:10.1038/s41556-020-0489-9 · Cited by 71 · see citing works
Computational methods for the integrative analysis of single-cell data
Forcato, M., Romano, O., & Bicciato, S. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa042 · Cited by 71 · see citing works
Statistical 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
Jointly Embedding Multiple Single-Cell Omics Measurements
Liu, J., Huang, Y., Singh, R., Vert, J., & Noble, W. S. (2019) · PubMed · doi:10.4230/lipics.wabi.2019.10 · Cited by 67 · see citing works
Cuomo, A. S. E., Seaton, D. D., McCarthy, D. J., Martinez, I., Bonder, M. J., Garcia‐Bernardo, J., Amatya, S., Madrigal, P., Isaacson, A., Buettner, F., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/630996 · Cited by 57 · see citing works
Self-citationSCIM: universal single-cell matching with unpaired feature sets
Stark, S. G., Ficek, J., Locatello, F., Bonilla, X., Chevrier, S., Singer, F., Aebersold, R. H., Alquaddoomi, F., Albinus, J. B., Alborelli, I., et al. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa843 · Cited by 53 · see citing works
Peng, G., Cui, G., Ke, J., & Jing, N. (2020) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-120219-083220 · Cited by 42 · cited 1× · see citing works
“…‘omics’ technology can fully capture the intricacy of complex molecular mechanisms, collectively, these data have the potential to provide a comprehensive picture of basic biological processes such as early mammalian development 1,2 , as well as clinically relevant traits, including cancer 1 .”
BackgroundRubin, A. J., Parker, K. R., Satpathy, A. T., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/414870 · Cited by 39 · see citing works
Query to reference single-cell integration with transfer learningPreprint
Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Avsec, Ž., Misharin, A. V., & Theis, F. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.16.205997 · Cited by 38 · see citing works
Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneityPreprint
Liu, L., Liu, C., Quintero, A., Wu, L. L., Yuan, Y., Wang, M., Cheng, M., Leng, L., Xu, L., Dong, G., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/316208 · Cited by 37 · see citing works
Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiationPreprint
Jerber, J., Seaton, D. D., Cuomo, A. S. E., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.21.103820 · Cited by 36 · see citing works
Self-citationA Single-Cell Tumor Immune Atlas for Precision OncologyPreprint
Nieto, P., Elosua-Bayés, M., Trincado, J. L., Marchese, D., Massoni-Badosa, R., Salvany, M., Henriques, A. R., Mereu, E., Moutinho, C., Ruiz, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.26.354829 · Cited by 30 · see citing works
Luo, C., Liu, H., Xie, F.-M., et al. (2019) · bioRxiv · doi:10.1101/2019.12.11.873398 · Cited by 27 · see citing works
Identifying temporal and spatial patterns of variation from multi-modal data using MEFISTOPreprint
Velten, B., Braunger, J. M., Arnol, D., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.03.366674 · Cited by 27 · see citing works
Furlotte, N. A., Kang, H. M., Ye, C., & Eskin, E. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr221 · Cited by 26 · see citing works
Single-cell sequencing edges into clinical trials
Keener, A. B. (2019) · Nature Medicine · doi:10.1038/d41591-019-00017-6 · Cited by 17 · see citing works
González‐Blas, C. B., Quan, X., Duran‐Romaña, R., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.19.882381 · Cited by 17 · see citing works
Swanson, E. G., Lord, C., Reading, J., Heubeck, A. T., Savage, A. K., Green, R. R., Li, X., Torgerson, T. R., Bumol, T. F., Graybuck, L. T., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.04.283887 · Cited by 11 · see citing works
SPICEMIX: Integrative single-cell spatial modeling of cell identityPreprint
Chidester, B., Zhou, T., Alam, S., & Ma, J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.29.383067 · Cited by 11 · see citing works
Dynamic pseudo-time warping of complex single-cell trajectoriesPreprint
Do, V. H., Blažević, M., Monteagudo-Mesas, P., Borozan, L., Elbassioni, K., Laue, S., Rojas, F., Matijević, D., & Canzar, S. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/522672 · Cited by 10 · see citing works
SCIM: Universal Single-Cell Matching with Unpaired Feature SetsPreprint
Stark, S. G., Ficek, J., Locatello, F., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.11.146845 · Cited by 10 · see citing works
Nerve conduction biopsy correlation in over 100 consecutive patients with suspected neuropathy.
Fisher, M. A. (1995) · PubMed · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Methods and applications for single-cell and spatial multi-omics
Vandereyken, K., Sifrim, A., Thienpont, B., & Voet, T. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00580-2 · Cited by 1,244 · see citing works
Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051 · see citing works
“Whereas BCRs directly recognize soluble or membrane-bound epitopes , TCRs interact with linear peptides bound to cell-surface major histocompatibility complex (MHC) molecules.”
BackgroundMulti-omics single-cell data integration and regulatory inference with graph-linked embedding
Cao, Z.-J., & Gao, G. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01284-4 · Cited by 616 · see citing works
MultiVI: deep generative model for the integration of multi-modal data
Ashuach, T., Gabitto, M. I., Jordan, M. I., & Yosef, N. (2021) · doi:10.1101/2021.08.20.457057 · Cited by 385 · see citing works
Single-cell proteomics enabled by next-generation sequencing or mass spectrometry
Bennett, H. M., Stephenson, W., Rose, C. M., & Darmanis, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01791-5 · Cited by 351 · see citing works
A harmonized atlas of mouse spinal cord cell types and their spatial organization
Russ, D. E., Cross, R. B. P., Li, L., Koch, S. C., Matson, K. J. E., Yadav, A., Alkaslasi, M. R., Lee, D. I., Le Pichon, C. E., Menon, V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25125-1 · Cited by 255 · see citing works
MUON: multimodal omics analysis framework
Bredikhin, D., Kats, I., & Stegle, O. (2022) · Genome Biology · doi:10.1186/s13059-021-02577-8 · Cited by 216 · see citing works
Self-citationProgram, C. S.-C. B., Abdulla, S., Aevermann, B., Assis, P., Badajoz, S., Bell, S. M., Bezzi, E., Cakir, B., Chaffer, J., Chambers, S., et al. (2023) · doi:10.1101/2023.10.30.563174 · Cited by 142 · see citing works
Single-cell genomics meets human genetics
Cuomo, A. S. E., Nathan, A., Raychaudhuri, S., MacArthur, D. G., & Powell, J. E. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00599-5 · Cited by 124 · see citing works
Self-citationZero-shot evaluation reveals limitations of single-cell foundation models
Kedzierska, K. Z., Crawford, L., Amini, A. P., & Lu, A. X. (2025) · Genome Biology · doi:10.1186/s13059-025-03574-x · Cited by 109 · see citing works
Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS
He, Z., Hu, S., Chen, Y., An, S., Zhou, J., Liu, R., Shi, J., Wang, J., Dong, G., Shi, J., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02040-y · Cited by 84 · see citing works
Multimodal single cell data integration challenge: results and lessons learned
Lance, C., Luecken, M. D., Burkhardt, D. B., Cannoodt, R., Rautenstrauch, P., Laddach, A., Ubingazhibov, A., Cao, Z.-J., Deng, K., Khan, S., et al. (2022) · doi:10.1101/2022.04.11.487796 · Cited by 81 · see citing works
Computational methods for single-cell multi-omics integration and alignment
Stanojevic, S., Li, Y., Ristivojevic, A., & Garmire, L. X. (2022) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.11.013 · Cited by 74 · see citing works
Assessing the limits of zero-shot foundation models in single-cell biology
Kedzierska, K. Z., Crawford, L., Amini, A. P., & Lu, A. X. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.16.561085 · Cited by 61 · see citing works
“For instance, biologists often cluster latent representations of single-cell gene expressions to discover new cell types without pre-existing knowledge or imposed bias on the discovery process [13, 14, 15].”
BackgroundWang, G., Zhao, J., Lin, Y., Liu, T., Zhao, Y., & Zhao, H. (2025) · Nature Communications · doi:10.1038/s41467-025-60333-z · Cited by 29 · see citing works
StabMap: Mosaic single cell data integration using non-overlapping features
Ghazanfar, S., Guibentif, C., & Marioni, J. C. (2022) · doi:10.1101/2022.02.24.481823 · Cited by 16 · see citing works
Self-citation
- Authors
- Argelaguet, R., Cuomo, A. S. E., Stegle, O., & Marioni, J. C.
- Affiliations
- Babraham Institute (GB)
- European Bioinformatics Institute (GB)
- German Cancer Research Center (DE)
- Corresponding author
- Argelaguet, Ricard
- Cuomo, Anna S. E.
- Stegle, Oliver
- Marioni, John C.
- Venue
- Nature Biotechnology
- Published
- 2021-05-03
- Volume
- 39
- Issue
- 10
- Pages
- 1202-1215
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W3159800610
- Cited by
- 472
- References
- 156
- Citations by year
- 2026: 103 · 2025: 89 · 2024: 94 · 2023: 94 · 2022: 71 · 2021: 21
- FWCI
- 7.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- EMBL International PhD Programme
- European Commission (810296)
- Core funding from EMBL and DKFZ
- Cancer Research UK (C9545/A29580)
- Core funding from CRUK (grant number listed above) and EMBL
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- single-cell data integration, batch effect correction, chromatin accessibility, transcription, cellular heterogeneity, integration strategies
- MeSH terms
- Animals, Chromatin, Data Interpretation, Statistical, Humans, Genetic Variation, Computational Biology, Genomics, Precision Medicine, Single-Cell Analysis
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
