The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Deep generative modeling for single-cell transcriptomics
Open accessPublished version of a preprint
Abstract
Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells ( https://github.com/YosefLab/scVI ). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Importantly, both Harmony’s and scVI’s design inherently incorporates batch labels in its training process.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [20]
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
“On the other hand, the transcriptional state of individual cells may vary in ways that are poorly captured in pseudo-bulk (for example, due to the presence of multiple cell types)38.”
From Transcriptome-wide analysis of differential expression in perturbation atlases (2025) · cites this paper as [38]
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
“Both scGPT and Geneformer produce cell embeddings intended to project potentially noisy gene expression measurements to a more biologically relevant latent space [18–21], and then these cell embeddings are fine-tuned for cell type classification.”
From Zero-shot evaluation reveals limitations of single-cell foundation models (2025) · cites this paper as [20]
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
“For instance, in scRNA-seq analysis, zero-inflated autoencoders were introduced to handle data sparsity by employing a specialized loss function that aligns with the binomial distribution of single-cell data [33].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [33]
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
“Multi-omics integration represents another natural application area, where specialized agents analyze transcriptomic, epigenomic, proteomic, and spatial datasets, reconciling heterogeneous signals through shared representation spaces, a task particularly suited to foundation models capable of cross-modal alignment [12, 43, 44].”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [43]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“To map SEA-AD data to cell types consistently across all 84 donors, we first defined robust transcriptional types, named supertypes, in the BICCN reference; 125 supertypes represented cell types that could be reliably reidentified in reference datasets (mean F1 score = 0.91) using hierarchical probabilistic Bayesian mapping47,48(Extended Data Fig. 5a,b).”
From Integrated multimodal cell atlas of Alzheimer’s disease (2024) · cites this paper as [47]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Importantly, statistical and deep-learning methods designed for single-cell RNA-seq data often utilize a negative binomial (or zero-inflated negative binomial) error model [10, 15].”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [10]
In the citing paper
“Latent representations, as learned by frameworks such as single-cell variational inference41, can be used for batch correction, clustering, visualization and DGE analysis.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [41]
In the citing paper
“Recently, deep learning has been used to produce an embedded workflow that can fit the data, denoise it and perform downstream analysis such as clustering and differential expression within the framework of the model (scVI: Lopez et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Lopez et al, 2018
In the citing paper
“Current proposed single-cell foundation models produce cell embeddings. These embeddings are intended to project potentially noisy gene expression measurements to a more biologically relevant latent space and to thus improve our ability to resolve cell types, consistent with previous machine learning methods in this field (including scVI) [30, 31, 24, 32].”
From Assessing the limits of zero-shot foundation models in single-cell biology (2023) · cites this paper as [24]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J. (1987) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(87)90125-7 · Cited by 21,392
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Data Analysis Using Regression and Multilevel/Hierarchical Models
Gelman, A., & Hill, J. (2006) · doi:10.1017/cbo9780511790942 · Cited by 13,597
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
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
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
Research works printed but not linked: 1
- Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K. & Winther, O. Ladder variational autoencoders. In Advances in Neural Information Processing Systems (eds Lee, D. D. et al.) 3738–3746 (NIPS Foundation, La Jolla, CA, 2016).
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
Dictionary learning for integrative, multimodal and scalable single-cell analysis
Hao, Y., Stuart, T., Kowalski, M. H., Choudhary, S., Hoffman, P., Hartman, A., Srivastava, A., Molla, G., Madad, S., Fernandez-Granda, C., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01767-y · Cited by 5,631
Hafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479
“Importantly, statistical and deep-learning methods designed for single-cell RNA-seq data often utilize a negative binomial (or zero-inflated negative binomial) error model [10, 15].”
MethodOpportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310
Current best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231
Wolf, F. A., Hamey, F. K., Plass, M., Solana, J., Dahlin, J. S., Göttgens, B., Rajewsky, N., Simon, L., & Theis, F. J. (2019) · Genome Biology · doi:10.1186/s13059-019-1663-x · Cited by 1,698
Adams, T. S., Schupp, J. C., Poli, S., Ayaub, E. A., Neumark, N., Ahangari, F., Chu, S. G., Raby, B. A., DeIuliis, G., Januszyk, M., et al. (2020) · Science Advances · doi:10.1126/sciadv.aba1983 · Cited by 1,420
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
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
scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI
Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., & Wang, B. (2023) · doi:10.1101/2023.04.30.538439 · Cited by 1,694
“234 In our benchmarking experiments, we compared scGPT with three popular integration methods: 235 scVI [41], Seurat [42], and Harmony [43].”
MethodDeep learning: new computational modelling techniques for genomics
Eraslan, G., Avsec, Ž., Gagneur, J., & Theis, F. J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0122-6 · Cited by 1,288
Bayesian statistics and modelling
van de Schoot, R., Depaoli, S. A., King, R., Kramer, B. M. R., Märtens, K., Tadesse, M. G., Vannucci, M., Gelman, A., Veen, D., Willemsen, J. E., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-020-00001-2 · Cited by 1,338
Cross-tissue immune cell analysis reveals tissue-specific features in humans
Conde, C. D., Xu, C., Jarvis, L. B., Rainbow, D. B., Wells, S. B., Gomes, T., Howlett, S. K., Suchánek, O., Polański, K., King, H. W., et al. (2022) · Science · doi:10.1126/science.abl5197 · Cited by 1,246
Self-citationBest 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
Single-cell RNA-seq denoising using a deep count autoencoder
Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S., & Theis, F. J. (2019) · Nature Communications · doi:10.1038/s41467-018-07931-2 · Cited by 1,084
MOFA+: 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
The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans
The Tabula Sapiens Consortium*, Jones, R. C., Karkanias, J., Krasnow, M. A., Pisco, A. O., Quake, S. R., Salzman, J., Yosef, N., Bulthaup, B., Brown, P., et al. (2022) · Science · doi:10.1126/science.abl4896 · Cited by 1,056
Self-citationGuidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)
Cossarizza, A., Chang, H., Radbruch, A., Acs, A., Adam, D., Adam‐Klages, S., Agace, W. W., Aghaeepour, N., Akdiş, M., Allez, M., et al. (2019) · European Journal of Immunology · doi:10.1002/eji.201970107 · Cited by 1,007
Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches
Guilliams, M., Bonnardel, J., Haest, B., Vanderborght, B., Wagner, C., Remmerie, A., Bujko, A., Martens, L., Thoné, T., Browaeys, R., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.018 · Cited by 985
Fleming, S. J., Chaffin, M. D., Arduini, A., Akkad, A.-D., Banks, E., Marioni, J. C., Philippakis, A. A., Ellinor, P. T., & Babadi, M. (2023) · Nature Methods · doi:10.1038/s41592-023-01943-7 · Cited by 867
Deep learning in cancer diagnosis, prognosis and treatment selection
Tran, K. A., Kondrashova, O., Bradley, A., Williams, E. D., Pearson, J. V., & Waddell, N. (2021) · Genome Medicine · doi:10.1186/s13073-021-00968-x · Cited by 932
An integrated cell atlas of the lung in health and disease
Sikkema, L., Ramírez-Suástegui, C., Strobl, D. C., Gillett, T. E., Zappia, L., Madissoon, E., Markov, N. S., Zaragosi, L., Ji, Y., Ansari, M., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02327-2 · Cited by 930
Szabo, P. A., Levitin, H. M., Miron, M., Snyder, M. E., Senda, T., Yuan, J., Cheng, Y. L., Bush, E. C., Dogra, P., Thapa, P., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12464-3 · Cited by 756
scMC learns biological variation through the alignment of multiple single-cell genomics datasets
Zhang, L., & Nie, Q. H. (2021) · Genome biology · doi:10.1186/s13059-020-02238-2 · Cited by 861
A Python library for probabilistic analysis of single-cell omics data
Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Valiollah Pour Amiri, V., Hong, J., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01206-w · Cited by 789
Self-citationComparison and evaluation of statistical error models for scRNA-seq
Choudhary, S., & Satija, R. (2022) · Genome Biology · doi:10.1186/s13059-021-02584-9 · Cited by 634
A comparison of automatic cell identification methods for single-cell RNA sequencing data
Abdelaal, T., Michielsen, L., Cats, D., Hoogduin, D., Mei, H., Reinders, M. J. T., & Mahfouz, A. (2019) · Genome Biology · doi:10.1186/s13059-019-1795-z · Cited by 628
Long, Y., Ang, K. S., Li, M., Chong, K. L. K., Sethi, R., Zhong, C., Xu, H., Ong, Z., Sachaphibulkij, K., Chen, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36796-3 · Cited by 700
Xu, C., Lopez, R., Mehlman, E., Regier, J., Jordan, M. I., & Yosef, N. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209620 · Cited by 691
“For evaluation, we used reference sets of differentially expressed genes that were obtained from published bulk-level analysis of similar cell subsets (microarrays, (G € org € un et al , 2005; Nakaya et al , 2011), as in (Lopez et al , 2018)).”
InfluentialSelf-citationscGen predicts single-cell perturbation responses
Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-019-0494-8 · Cited by 661
Statistics or biology: the zero-inflation controversy about scRNA-seq data
Jiang, R., Sun, T., Song, D., & Li, J. J. (2022) · Genome Biology · doi:10.1186/s13059-022-02601-5 · Cited by 614
Large-scale foundation model on single-cell transcriptomics
Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Zhang, X., & Song, L. (2024) · Nature Methods · doi:10.1038/s41592-024-02305-7 · Cited by 628
“We then estimated the performance of various scale xTrimoGene architecture models with parameter sizes equivalent to previous transformer-based models 13–15 , and compared with scVI 25 (Supplementary Note 2).”
Multi-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
Mapping single-cell data to reference atlases by transfer learning
Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Wagenstetter, M., Avsec, Ž., Gayoso, A., Yosef, N., Interlandi, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01001-7 · Cited by 602
Self-citationJoint probabilistic modeling of single-cell multi-omic data with totalVI
Gayoso, A., Steier, Z., Lopez, R., Regier, J., Nazor, K. L., Streets, A., & Yosef, N. (2021) · Nature Methods · doi:10.1038/s41592-020-01050-x · Cited by 592
Self-citationThe Human and Mouse Enteric Nervous System at Single-Cell Resolution
Drokhlyansky, E., Smillie, C. S., Wittenberghe, N. V., Ericsson, M., Griffin, G. K., Eraslan, G., Dionne, D. A., Cuoco, M. S., Goder-Reiser, M. N., Sharova, T. Y., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.003 · Cited by 606
SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis
Wendisch, D., Dietrich, O., Mari, T., von Stillfried, S., Ibarra, I. L., Mittermaier, M., Mache, C., Chua, R. L., Knoll, R., Timm, S., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.033 · Cited by 528
Transcriptional and Cellular Diversity of the Human Heart
Tucker, N. R., Chaffin, M., Fleming, S. J., Hall, A. W., Parsons, V. A., Bedi, K. C., Akkad, A.-D., Herndon, C. N., Arduini, A., Papangeli, I., et al. (2020) · Circulation · doi:10.1161/circulationaha.119.045401 · Cited by 554
Interpretation of T cell states from single-cell transcriptomics data using reference atlases
Andreatta, M., Corría-Osorio, J., Müller, S., Cubas, R. A., Coukos, G., & Carmona, S. J. (2021) · Nature Communications · doi:10.1038/s41467-021-23324-4 · Cited by 558
Salcher, S., Sturm, G., Horvath, L., Untergasser, G., Kuempers, C., Fotakis, G., Panizzolo, E., Martowicz, A., Trebo, M., Pall, G., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.10.008 · Cited by 503
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model
Townes, F. W., Hicks, S. C., Aryee, M. J., & Irizarry, R. A. (2019) · Genome Biology · doi:10.1186/s13059-019-1861-6 · Cited by 471
Computational principles and challenges in single-cell data integration
Argelaguet, R., Cuomo, A. S. E., Stegle, O., & Marioni, J. C. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00895-7 · Cited by 472
scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
Wang, J., Ma, A., Chang, Y., Gong, J., Jiang, Y., Qi, R., Wang, C., Fu, H., Ma, Q., & Xu, D. H. (2021) · Nature Communications · doi:10.1038/s41467-021-22197-x · Cited by 459
Integrated single cell analysis of blood and cerebrospinal fluid leukocytes in multiple sclerosis
Schafflick, D., Xu, C. A., Hartlehnert, M., Cole, M. B., Schulte‐Mecklenbeck, A., Lautwein, T., Wolbert, J., Heming, M., Meuth, S. G., Kuhlmann, T., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-14118-w · Cited by 457
Self-citationSpatially resolved multiomics of human cardiac niches
Kanemaru, K., Cranley, J., Muraro, D., Miranda, A. M. A., Ho, S. Y., Wilbrey-Clark, A., Patrick Pett, J., Polanski, K., Richardson, L., Litvinukova, M., et al. (2023) · Nature · doi:10.1038/s41586-023-06311-1 · Cited by 425
Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis
Li, X., Wang, K., Lyu, Y., Pan, H., Zhang, J., Stambolian, D. E., Suszták, K., Reilly, M. P., Hu, G., & Li, M. (2020) · Nature Communications · doi:10.1038/s41467-020-15851-3 · Cited by 419
Integrated multimodal cell atlas of Alzheimer’s disease
Gabitto, M. I., Travaglini, K. J., Rachleff, V. M., Kaplan, E. S., Long, B., Ariza, J., Ding, Y., Mahoney, J. T., Dee, N., Goldy, J., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01774-5 · Cited by 397
“…all 84 donors, we first defined robust transcriptional types, named supertypes, in the BICCN reference; 125 supertypes represented cell types that could be reliably reidentified in reference datasets (mean F1 score = 0.91) using hierarchical probabilistic Bayesian mapping 47,48 (Extended Data Fig.”
InfluentialHow to build the virtual cell with artificial intelligence: Priorities and opportunities
Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.015 · Cited by 393
“59,78,79 Using AI algorithms to integrate different data modalities collected with sequencing and imaging technologies creates a multi-view model of the cell that can be both dynamic and predictive.”
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
Self-citationDroplet scRNA-seq is not zero-inflated
Svensson, V. (2019) · doi:10.1101/582064 · Cited by 356
Current progress and open challenges for applying deep learning across the biosciences
Sapoval, N., Aghazadeh, A., Nute, M., Antunes, D. A., Balaji, A., Baraniuk, R. G., Barberan, C., Dannenfelser, R., Dun, C., Edrisi, M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29268-7 · Cited by 381
Predicting transcriptional outcomes of novel multigene perturbations with GEARS
Roohani, Y., Huang, K., & Leskovec, J. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01905-6 · Cited by 355
Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution
Yang, D., Jones, M. G., Naranjo, S., Rideout, W. M., Min, K. H., Ho, R., Wu, W., Replogle, J. M., Page, J. L., Quinn, J. J., et al. (2022) · Cell · doi:10.1016/j.cell.2022.04.015 · Cited by 349
Self-citationSomatic mutations and cell identity linked by Genotyping of Transcriptomes
Nam, A. S., Kim, K.-T., Chaligne, R., Izzo, F., Ang, C., Taylor, J., Myers, R. M., Abu-Zeinah, G., Brand, R., Omans, N. D., et al. (2019) · Nature · doi:10.1038/s41586-019-1367-0 · Cited by 333
Spatial components of molecular tissue biology
Palla, G., Fischer, D. S., Regev, A., & Theis, F. J. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01182-1 · Cited by 318
Mapping the developing human immune system across organs
Suo, C., Dann, E., Goh, I., Jardine, L., Kleshchevnikov, V., Park, J.-E., Botting, R. A., Stephenson, E., Engelbert, J., Tuong, Z. K., et al. (2022) · Science · doi:10.1126/science.abo0510 · Cited by 323
The triumphs and limitations of computational methods for scRNA-seq
Kharchenko, P. V. (2021) · Nature Methods · doi:10.1038/s41592-021-01171-x · Cited by 321
Predicting cellular responses to complex perturbations in high‐throughput screens
Lotfollahi, M., Klimovskaia Susmelj, A., De Donno, C., Hetzel, L., Ji, Y., Ibarra, I. L., Srivatsan, S. R., Naghipourfar, M., Daza, R. M., Martin, B., et al. (2023) · Molecular Systems Biology · doi:10.15252/msb.202211517 · Cited by 307
Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts
Quinn, J. J., Jones, M. G., Okimoto, R. A., Nanjo, S., Chan, M. M., Yosef, N., Bivona, T. G., & Weissman, J. S. (2021) · Science · doi:10.1126/science.abc1944 · Cited by 319
Self-citationMolecular landscapes of human hippocampal immature neurons across lifespan
Zhou, Y. F., Su, Y., Li, S., Kennedy, B. C., Zhang, D. Y., Bond, A. M., Sun, Y. Y., Jacob, F., Lu, L., Hu, P., et al. (2022) · Nature · doi:10.1038/s41586-022-04912-w · Cited by 314
Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data
Andrews, T. S., Kiselev, V. Y., McCarthy, D. J., & Hemberg, M. (2020) · Nature Protocols · doi:10.1038/s41596-020-00409-w · Cited by 323
Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics
Gulati, G. S., D'Silva, J. P., Liu, Y., Wang, L., & Newman, A. M. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00768-2 · Cited by 347
A systematic evaluation of single-cell RNA-sequencing imputation methods
Hou, W., Ji, Z., Ji, H., & Hicks, S. C. (2020) · Genome biology · doi:10.1186/s13059-020-02132-x · Cited by 307
Gao, Y., Li, J., Cheng, W., Diao, T., Liu, H., Bo, Y., Liu, C., Zhou, W., Chen, M., Zhang, Y., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.08.020 · Cited by 320
Solo: Doublet Identification in Single-Cell RNA-Seq via Semi-Supervised Deep Learning
Bernstein, N. J., Fong, N. L., Lam, I., Roy, M. A., Hendrickson, D. G., & Kelley, D. R. (2020) · Cell Systems · doi:10.1016/j.cels.2020.05.010 · Cited by 280
Automatic cell-type harmonization and integration across Human Cell Atlas datasets
Xu, C., Prete, M., Webb, S., Jardine, L., Stewart, B. J., Hoo, R., He, P., Meyer, K. B., & Teichmann, S. A. (2023) · Cell · doi:10.1016/j.cell.2023.11.026 · Cited by 321
Comprehensive cell atlas of the first-trimester developing human brain
Braun, E., Danan-Gotthold, M., Borm, L. E., Vinsland, E., Lee, K. W., Lönnerberg, P., Hu, L., Li, X., He, X., Andrusivová, Ž., et al. (2023) · Science · doi:10.1126/science.adf1226 · Cited by 300
Machine learning for antimicrobial peptide identification and design
Wan, F., Wong, F. J., Collins, J. J., & de la Fuente‐Núñez, C. (2024) · Nature Reviews Bioengineering · doi:10.1038/s44222-024-00152-x · Cited by 317
scVAE: variational auto-encoders for single-cell gene expression data
Grønbech, C. H., Vording, M. F., Timshel, P. N., Sønderby, C. K., Pers, T. H., & Winther, O. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa293 · Cited by 277
The single-cell epigenomic and transcriptional landscape of immunity to influenza vaccination
Wimmers, F., Donato, M., Kuo, A. J., Ashuach, T., Gupta, S., Li, C., Dvorak, M., Foecke, M. H., Chang, S. E., Hagan, T., et al. (2021) · Cell · doi:10.1016/j.cell.2021.05.039 · Cited by 275
Self-citationXu, H., Ding, J., Porter, C. B. M., Wallrapp, A., Tabaka, M., Ma, S., Fu, S., Guo, X., Riesenfeld, S. J., Su, C., et al. (2019) · Immunity · doi:10.1016/j.immuni.2019.09.004 · Cited by 273
DestVI identifies continuums of cell types in spatial transcriptomics data
Lopez, R., Li, B., Keren-Shaul, H., Boyeau, P., Kedmi, M., Pilzer, D., Jelinski, A., Yofe, I., David, E., Wagner, A., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01272-8 · Cited by 276
“The dispersion parameter θg for every gene g is estimated from the data using scVI [29].”
MethodInfluentialSelf-citationDental cell type atlas reveals stem and differentiated cell types in mouse and human teeth
Křivánek, J., Soldatov, R., Kastriti, M. E., Chontorotzea, T., Herdina, A. N., Petersen, J., Szarowská, B., Landová, M., Matejova, V. K., Hollá, L. I., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18512-7 · Cited by 274
Hotspot identifies informative gene modules across modalities of single-cell genomics
DeTomaso, D., & Yosef, N. (2021) · Cell Systems · doi:10.1016/j.cels.2021.04.005 · Cited by 267
Self-citationHypoMap—a unified single-cell gene expression atlas of the murine hypothalamus
Steuernagel, L., Lam, B. Y. H., Klemm, P., Dowsett, G. K. C., Bauder, C. A., Tadross, J. A., Hitschfeld, T. S., del Rio Martin, A., Chen, W., de Solis, A. J., et al. (2022) · Nature Metabolism · doi:10.1038/s42255-022-00657-y · Cited by 267
Functional interpretation of single cell similarity maps
DeTomaso, D., Jones, M. G., Subramaniam, M., Ashuach, T., Ye, C., & Yosef, N. (2019) · Nature Communications · doi:10.1038/s41467-019-12235-0 · Cited by 259
Self-citationDeep learning for inferring gene relationships from single-cell expression data
Yuan, Y., & Bar‐Joseph, Z. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1911536116 · Cited by 270
A RORγt+ cell instructs gut microbiota-specific Treg cell differentiation
Kedmi, R., Najar, T. A., Mesa, K. R., Grayson, A., Kroehling, L., Hao, Y., Hao, S., Pokrovskii, M. V., Xu, M., Talbot, J., et al. (2022) · Nature · doi:10.1038/s41586-022-05089-y · Cited by 270
Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes
Armand, E. J., Li, J., Xie, F., Luo, C., & Mukamel, E. A. (2021) · Neuron · doi:10.1016/j.neuron.2020.12.010 · Cited by 241
Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis
Li, Y., Xin, W., Yang, P., Jiang, G., & Luo, Y. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.11.003 · Cited by 287
Artificial intelligence in cancer research, diagnosis and therapy
Elemento, O., Leslie, C., Lundin, J., & Tourassi, G. (2021) · Nature Reviews Cancer · doi:10.1038/s41568-021-00399-1 · Cited by 264
Spatially aware dimension reduction for spatial transcriptomics
Shang, L., & Zhou, X. (2022) · Nature Communications · doi:10.1038/s41467-022-34879-1 · Cited by 256
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
Korsunsky, I., Wei, K., Pohin, M., Kim, E. Y., Barone, F., Major, T., Taylor, E., Ravindran, R. P., Kemble, S., Watts, G. F. M., et al. (2022) · Med · doi:10.1016/j.medj.2022.05.002 · Cited by 249
ACTINN: automated identification of cell types in single cell RNA sequencing
Ma, F., & Pellegrini, M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz592 · Cited by 252
Concepts and limitations for learning developmental trajectories from single cell genomics
Tritschler, S., Büttner, M., Fischer, D. S., Lange, M., Bergen, V., Lickert, H., & Theis, F. J. (2019) · Development · doi:10.1242/dev.170506 · Cited by 242
Herring, C. A., Simmons, R. K., Freytag, S., Poppe, D., Moffet, J. J. D., Pflueger, J., Buckberry, S., Vargas-Landín, D. B., Clément, O., Goñi, E., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.039 · Cited by 242
Learning single-cell perturbation responses using neural optimal transport
Bunne, C., Stark, S. G., Gut, G., del Castillo, J. S., Levesque, M., Lehmann, K.-V., Pelkmans, L., Krause, A., & Rätsch, G. (2023) · Nature Methods · doi:10.1038/s41592-023-01969-x · Cited by 202
Efficient and precise single-cell reference atlas mapping with Symphony
Kang, J. B., Nathan, A., Weinand, K., Zhang, F., Millard, N., Rumker, L., Moody, D. B., Korsunsky, I., & Raychaudhuri, S. (2021) · Nature Communications · doi:10.1038/s41467-021-25957-x · Cited by 246
A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication
Cheng, C., Chen, W., Jin, H., & Chen, X. '. (2023) · Cells · doi:10.3390/cells12151970 · Cited by 253
Rojahn, T. B., Vorstandlechner, V., Krausgruber, T., Bauer, W. M., Alkon, N., Bangert, C., Thaler, F. M., Sadeghyar, F., Fortelny, N., Fife, V. E. R., et al. (2020) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2020.03.041 · Cited by 233
Sarkar, A., & Stephens, M. (2021) · Nature Genetics · doi:10.1038/s41588-021-00873-4 · Cited by 224
SpatialData: an open and universal data framework for spatial omics
Marconato, L., Palla, G., Yamauchi, K. A., Virshup, I., Heidari, E., Treis, T., Vierdag, W.-M., Toth, M., Stockhaus, S., Shrestha, R., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02212-x · Cited by 253
Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data
Xi, N. M., & Li, J. J. (2021) · Cell Systems · doi:10.1016/j.cels.2020.11.008 · Cited by 208
He, P., Lim, K., Sun, D., Pett, J. P., Jeng, Q., Polański, K., Dong, Z., Bolt, L., Richardson, L., Mamanova, L., et al. (2022) · Cell · doi:10.1016/j.cell.2022.11.005 · Cited by 239
Baião, A. R., Cai, Z., Poulos, R. C., Robinson, P. J., Reddel, R. R., Zhong, Q., Vinga, S., & Gonçalves, E. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf355 · Cited by 274
SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data
Persad, S., Choo, Z.-N., Dien, C., Sohail, N., Masilionis, I., Chaligné, R., Nawy, T., Brown, C. C., Sharma, R., Pe’er, I., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01716-9 · Cited by 222
A spatially resolved atlas of the human lung characterizes a gland-associated immune niche
Madissoon, E., Oliver, A. J., Kleshchevnikov, V., Wilbrey-Clark, A., Polanski, K., Richoz, N., Ribeiro Orsi, A., Mamanova, L., Bolt, L., Elmentaite, R., et al. (2023) · Nature Genetics · doi:10.1038/s41588-022-01243-4 · Cited by 219
Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data
Lause, J., Berens, P., & Kobak, D. (2021) · Genome Biology · doi:10.1186/s13059-021-02451-7 · Cited by 195
Metadata
- Authors
- Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N.
- Affiliations
- University of California, Berkeley (US)
- Ragon Institute of MGH, MIT and Harvard (US)
- Corresponding author
- Yosef, Nir
- Venue
- Nature Methods
- Published
- 2018-11-30
- Volume
- 15
- Issue
- 12
- Pages
- 1053-1058
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC6289068
- OpenAlex
- W2901677030
- Cited by
- 2,530
- References
- 55
- Citations by year
- 2026: 588 · 2025: 489 · 2024: 409 · 2023: 311 · 2022: 248 · 2021: 239 · 2020: 155 · 2019: 80 · 2018: 11
- FWCI
- 43.21
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México
- National Institute of Allergy and Infectious Diseases (R38AI140299, U19AI090023)
- 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 › Gene Regulatory Network Analysis
- Keywords
- single-cell transcriptomics, single-cell RNA sequencing, deep generative models, batch effect correction, differential gene expression, cell clustering, uncertainty quantification
- MeSH terms
- Algorithms, Animals, Brain, Hematopoietic Stem Cells, Humans, Leukocytes, Mononuclear, Models, Biological, Genetic Variation, Cluster Analysis, Sequence Analysis, RNA, Computational Biology, 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
