Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species
Open accessAbstract
Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been proposed based on statistical, mechanistic and machine learning models in specific settings, no generalization of predictions to phenomena absent from training data (‘out-of-sample’) has yet been demonstrated. Here, we present scGen, a model combining variational autoencoders and latent space vector arithmetics for high-dimensional single-cell gene expression data. In benchmarks across a broad range of examples, we show that scGen accurately models dose and infection response of cells across cell types, studies and species. In particular, we demonstrate that scGen learns cell type and species specific response implying that it captures features that distinguish responding from non-responding genes and cells. With the upcoming availability of large-scale atlases of organs in healthy state, we envision scGen to become a tool for experimental design through in silico screening of perturbation response in the context of disease and drug treatment.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
First applications of deep learning to scRNA-seq are starting to emerge from dimensionality reduction to denoising.
“First applications to scRNA‐seq are starting to emerge from dimensionality reduction to denoising (e.g. scVis: Ding et al, 2018; scGen: preprint: Lotfollahi et al, 2018; DCA: Eraslan et al, 2019).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Lotfollahi et al, 2018
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
(2014) · Cited by 33,419 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568 · see citing works
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602 · see citing works
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930 · see citing works
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,089 · see citing works
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,369 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174 · see citing works
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235 · see citing works
UMAP: Uniform Manifold Approximation and Projection
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826 · see citing works
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 · see citing works
Self-citationMassively 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 · see citing works
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101 · see citing works
International Conference on Learning Representations (ICLR 2013)
松尾, 豊. (2013) · 人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence · Cited by 6,286 · see citing works
Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt
Durinck, S., Spellman, P. T., Birney, E., & Huber, W. K. (2009) · Nature Protocols · doi:10.1038/nprot.2009.97 · Cited by 4,081 · see citing works
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
The Tabula Muris Consortium, coordination, O., coordination, L., processing, O. C. A., sequencing, L. P. A., analysis, C. D., annotation, C. T., Writing group, Supplemental text writing group, & Principal investigators (2018) · Nature · doi:10.1038/s41586-018-0590-4 · Cited by 3,326 · see citing works
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124 · 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
Regev, 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 · 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
A single-cell survey of the small intestinal epithelium
Haber, A. L., Biton, M., Rogel, N., Herbst, R. H., Shekhar, K., Smillie, C., Burgin, G., Delorey, T. M., Howitt, M. R., Katz, Y., et al. (2017) · Nature · doi:10.1038/nature24489 · Cited by 1,750 · see citing works
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 · see citing works
Self-citationA comparison of single-cell trajectory inference methods
Saelens, W., Cannoodt, R., Todorov, H., & Saeys, Y. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0071-9 · Cited by 1,670 · see citing works
Mapping the Mouse Cell Atlas by Microwell-Seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.001 · Cited by 1,597 · 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
Baron, M., Veres, A., Wolock, S. L., Faust, A. L., Gaujoux, R., Vetere, A., Ryu, J. H., Wagner, B. K., Shen-Orr, S. S., Klein, A. M., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.08.011 · Cited by 1,508 · see citing works
Tutorial on Variational Autoencoders
Doersch, C. (2016) · arXiv · doi:10.48550/arxiv.1606.05908 · Cited by 1,470 · 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
Coupled Generative Adversarial NetworksPreprint
Liu, M.-Y., & Tuzel, O. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.07536 · Cited by 1,433 · 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
Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells
Deng, Q., Ramsköld, D., Reinius, B., & Sandberg, R. (2014) · Science · doi:10.1126/science.1245316 · Cited by 1,345 · 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
A 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
Adamson, B., Norman, T. M., Jost, M., Cho, M. Y., Nuñez, J. K., Chen, Y., Villalta, J. E., Gilbert, L. A., Horlbeck, M. A., Hein, M. Y., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.048 · Cited by 1,193 · see citing works
FUNDAMENTALS OF PLANARIAN REGENERATION
Reddien, P. W., & Alvarado, A. S. (2004) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev.cellbio.20.010403.095114 · Cited by 1,108 · see citing works
Pooled 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 · see citing works
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 · see citing works
Self-citationSingle-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease
Park, J., Shrestha, R., Qiu, C., Kondo, A., Huang, S., Werth, M., Li, M., Barasch, J., & Suszták, K. (2018) · Science · doi:10.1126/science.aar2131 · Cited by 1,065 · 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
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 · 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
Single-cell transcriptomics to explore the immune system in health and disease
Stubbington, M. J. T., Rozenblatt–Rosen, O., Regev, A., & Teichmann, S. A. (2017) · Science · doi:10.1126/science.aan6828 · Cited by 560 · see citing works
Single-Cell Transcriptomics of the Human Endocrine Pancreas
Wang, Y. J., Schug, J., Won, K., Liu, C., Naji, A., Avrahami, D., Golson, M. L., & Kaestner, K. H. (2016) · Diabetes · doi:10.2337/db16-0405 · Cited by 434 · see citing works
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models
Ding, J., Condon, A., & Shah, S. P. (2018) · Nature Communications · doi:10.1038/s41467-018-04368-5 · Cited by 337 · see citing works
Mohammed, H., Hernando-Herraez, I., Savino, A., Scialdone, A., Macaulay, I. C., Mulas, C., Chandra, T., Voet, T., Dean, W., Nichols, J., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.07.009 · Cited by 324 · 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
Marouf, M., Machart, P., Bansal, V., Kilian, C., Magruder, D. S., Krebs, C. F., & Bonn, S. (2020) · Nature Communications · doi:10.1038/s41467-019-14018-z · Cited by 256 · see citing works
Way, G. P., & Greene, C. S. (2017) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813235533_0008 · Cited by 256 · see citing works
A single-cell hematopoietic landscape resolves 8 lineage trajectories and defects in Kit mutant mice
Dahlin, J. S., Hamey, F. K., Pijuan-Sala, B., Shepherd, M., Lau, W. W. Y., Nestorowa, S., Weinreb, C., Wolock, S., Hannah, R., Diamanti, E., et al. (2018) · Blood · doi:10.1182/blood-2017-12-821413 · Cited by 233 · see citing works
Gene expression variability across cells and species shapes innate immunity
Hagai, T., Chen, X., Miragaia, R. J., Rostom, R., Gomes, T., Kunowska, N., Henriksson, J., Park, J.-E., Proserpio, V., Donati, G., et al. (2018) · Nature · doi:10.1038/s41586-018-0657-2 · Cited by 220 · see citing works
Single cells make big data: New challenges and opportunities in transcriptomics
Angerer, P., Simon, L. M., Tritschler, S., Wolf, F., Fischer, D. S., & Theis, F. J. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.07.004 · Cited by 213 · see citing works
Self-citationKernfeld, E. M., Genga, R. M. J., Neherin, K., Magaletta, M. E., Xu, P., & Maehr, R. (2018) · Immunity · doi:10.1016/j.immuni.2018.04.015 · Cited by 186 · see citing works
Fröhlich, F., Kessler, T., Weindl, D., Shadrin, A., Schmiester, L., Hache, H., Muradyan, A., Schütte, M., Lim, J.-H., Heinig, M., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2018.10.013 · Cited by 143 · see citing works
Self-citationWay, G. P., & Greene, C. S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/174474 · Cited by 103 · see citing works
A comparison of single-cell trajectory inference methods: towards more accurate and robust toolsPreprint
Saelens, W., Cannoodt, R., Todorov, H., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/276907 · Cited by 98 · see citing works
Generative adversarial networks simulate gene expression and predict perturbations in single cellsPreprint
Ghahramani, A., Watt, F. M., & Luscombe, N. M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/262501 · Cited by 98 · see citing works
Single cell RNA-seq denoising using a deep count autoencoderPreprint
Eraslan, G., Simon, L. M., Mircea, M., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/300681 · Cited by 86 · see citing works
Wolf, F. A., Hamey, F., Plass, M., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/208819 · Cited by 48 · see citing works
Self-citationPark, J.-E., Polański, K., Meyer, K. B., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/397042 · Cited by 36 · see citing works
MAGAN: Aligning Biological ManifoldsPreprint
Amodio, M., & Krishnaswamy, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.00385 · Cited by 33 · see citing works
Clift, M. J. D., Fytianos, K., Vanhecke, D., Hočevar, S., Petri‐Fink, A., & Rothen‐Rutishauser, B. (2017) · Scientific Reports · doi:10.1038/s41598-017-00369-4 · Cited by 28 · see citing works
Panoramic stitching of heterogeneous single-cell transcriptomic dataPreprint
Hie, B., Bryson, B. D., & Berger, B. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/371179 · Cited by 21 · see citing works
Rewiring of the cellular and inter-cellular landscape of the human colon during ulcerative colitisPreprint
Smillie, C. S., Biton, M., Ordovás-Montañés, J., Sullivan, K. M., Burgin, G., Graham, D. B., Herbst, R. H., Rogel, N., Slyper, M., Waldman, J., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/455451 · Cited by 17 · see citing works
Marouf, M., Machart, P., Bansal, V., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/390153 · Cited by 13 · see citing works
Neuron Interference: Evidence-Based Batch Effect RemovalPreprint
Amodio, M., Montgomery, R. R., Pappalardo, J. L., Hafler, D. A., & Krishnaswamy, S. (2018) · arXiv (Cornell University) · Cited by 8 · see citing works
Inferring Reaction Networks using Perturbation DataPreprint
Choi, K., Hellerstein, J., Wiley, H. S., & Sauro, H. M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/351767 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- Froehlich, F. et al. Efficient parameterization of large-scale mechanistic models enables drug response prediction for cancer cell lines. bioRxiv 174094 (2017).
- Ghahramani, A., Watt, F. M. & Luscombe, N. M. Generative adversarial networks uncover epidermal regulators and predict single cell perturbations. bioRxiv 262501 (2018).
- White, T. Sampling generative networks: Notes on a few effective techniques. arXiv 1609.04468 (2016). · arXiv
Cited by
Sorted by citations
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 · see citing works
Self-citationA benchmark of batch-effect correction methods for single-cell RNA sequencing data
Tran, H. T. N., Ang, K. S., Chevrier, M., Zhang, X., Lee, N. Y. S., Goh, M., & Chen, J. (2020) · Genome Biology · doi:10.1186/s13059-019-1850-9 · Cited by 1,113 · see citing works
Deep 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 1,229 · see citing works
Self-citationDeep packet: a novel approach for encrypted traffic classification using deep learning
Lotfollahi, M., Jafari Siavoshani, M., Shirali Hossein Zade, R., & Saberian, M. (2020) · Soft Computing · doi:10.1007/s00500-019-04030-2 · Cited by 1,190 · see citing works
Self-citationJoshi, N., Watanabe, S., Verma, R., Jablonski, R. P., Chen, C.-I., Cheresh, P., Markov, N. S., Reyfman, P. A., McQuattie-Pimentel, A. C., Sichizya, L., et al. (2020) · European Respiratory Journal · doi:10.1183/13993003.00646-2019 · Cited by 344 · see citing works
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 268 · see citing works
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 233 · see citing works
Self-citationEnhancing scientific discoveries in molecular biology with deep generative models
Lopez, R., Gayoso, A., & Yosef, N. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199198 · Cited by 82 · see citing works
Immunology Driven by Large-Scale Single-Cell Sequencing
Gomes, T., Teichmann, S. A., & Talavera-López, C. N. (2019) · Trends in Immunology · doi:10.1016/j.it.2019.09.004 · Cited by 71 · see citing works
SMILE: mutual information learning for integration of single-cell omics data
Xu, Y., Das, P., & McCord, R. P. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab706 · Cited by 58 · see citing works
Park, J., Kim, H., Kim, J., & Cheon, M. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008099 · Cited by 50 · see citing works
Strunz, M., Simon, L. M., Ansari, M., Mattner, L. F., Angelidis, I., Mayr, C. H., Kathiriya, J. J., Yee, M., Ogar, P., Sengupta, A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/705244 · Cited by 30 · see citing works
Self-citationDissecting the human immune system with single cell RNA sequencing technology
Xu, G., Liu, Y., Li, H., Liu, L., Zhang, S., & Zhang, Z. (2019) · Journal of Leukocyte Biology · doi:10.1002/jlb.5mr1019-179r · Cited by 18 · see citing works
BEENE: deep learning-based nonlinear embedding improves batch effect estimation
Rahman, M. A., Tutul, A. A., Sharmin, M., & Bayzid, M. S. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad479 · Cited by 9 · see citing works
SSBER: removing batch effect for single-cell RNA sequencing data
Zhang⋆, Y., & Wang, F. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04165-w · Cited by 8 · see citing works
Domain adaptation for supervised integration of scRNA-seq data
Sun, Y., & Qiu, P. (2023) · Communications Biology · doi:10.1038/s42003-023-04668-7 · Cited by 7 · see citing works
Interpretable Factors in scRNA-seq Data with Disentangled Generative Models
Mao, H., Broerman, M. J., & Benos, P. V. (2020) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe50027.2020.00022 · Cited by 1 · see citing works
Single-Cell RNA Sequencing Technologies
Gupta, M. K., Gouda, G., Donde, R., Sabarinathan, S., Goswami, P., Dash, G. K., Rajesh, N., Pati, P., Rathode, S. K., Vadde, R., et al. (2021) · Springer eBooks · doi:10.1007/978-981-16-3993-7_25 · Cited by 2 · see citing works
OmicPioneer-sc: an integrated, interactive visualization environment for single-cell sequencing dataPreprint
Weinstein, J. N., Rohrdanz, M. A., Stucky, M., Melott, J. M., Ma, J., Mohanty, V., Manyam, G. C., Wakefield, C., Chen, K., Navin, N. E., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.31.363580
Xu, Y., & Strick, A. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.12320
Baraban, A., Clark, B. S., Slosberg, J., Fertig, E. J., Goff, L. A., & Stein-O’Brien, G. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.25.457650
- Authors
- Lotfollahi, M., Wolf, F. A., & Theis, F. J.
- Affiliations
- Helmholtz Munich (DE)
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2018-11-29
- OpenAlex
- W2902382430
- Cited by
- 21
- References
- 67
- Citations by year
- 2023: 2 · 2021: 5 · 2020: 6 · 2019: 8
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Funders
- Deutsche Forschungsgemeinschaft (Subproject A17)
- 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 Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- variational autoencoder, single-cell gene expression, out-of-sample forecasting, dose-response relationship, infection response, cell type specificity, species specificity, virtual screening, experimental design
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
