Interpretable representation learning for 3D multi-piece intracellular structures using point clouds
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Abstract
A key challenge in understanding subcellular organization is quantifying interpretable measurements of intracellular structures with complex multi-piece morphologies in an objective, robust and generalizable manner. Here we introduce a morphology-appropriate representation learning framework that uses three-dimensional rotation-invariant autoencoders and point clouds. This framework is used to learn representations of complex shapes that are independent of orientation, compact and interpretable. We apply our framework to intracellular structures with punctate morphologies (for example, DNA replication foci) and polymorphic morphologies (for example, nucleoli). We explore the trade-offs in the performance of this framework compared to image-based autoencoders by performing multi-metric benchmarking across efficiency, generative capability and representation expressivity metrics. We find that the proposed framework, which embraces the underlying morphology of multi-piece structures, can facilitate the unsupervised discovery of subclusters for each structure. We show how this approach can also be applied to phenotypic profiling using a dataset of nucleolar images following drug perturbations.
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Cites
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
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886 · see citing works
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 21,793 · 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
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423 · see citing works
Masked Autoencoders Are Scalable Vision Learners
He, K., Chen, X., Xie, S., Li, Y., Dollar, P., & Girshick, R. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01553 · Cited by 8,489 · cited 1× · see citing works
“First, we pretrained a masked autoencoder 48 using a ZYX patch size of (2, 2, 2), a mask ratio of 0.75 and learnable positional embeddings.”
InfluentialDynamic Graph CNN for Learning on Point Clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M. (2019) · ACM Transactions on Graphics · doi:10.1145/3326362 · Cited by 7,199 · see citing works
CellProfiler: image analysis software for identifying and quantifying cell phenotypes
Carpenter, A. E., Jones, T. R., Lamprecht, M. R., Clarke, C., Kang, I. H., Friman, O., Guertin, D. A., Chang, J. H., Lindquist, R. A., Moffat, J., et al. (2006) · Genome biology · doi:10.1186/gb-2006-7-10-r100 · Cited by 5,077 · see citing works
Geometric Deep Learning: Going beyond Euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661 · see citing works
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Park, J. J., Florence, P., Straub, J., Newcombe, R., & Lovegrove, S. J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00025 · Cited by 3,557 · see citing works
Nonlinear principal component analysis using autoassociative neural networks
Kramer, M. A. (1991) · AIChE Journal · doi:10.1002/aic.690370209 · Cited by 3,037 · 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
A draft map of the human proteome
Kim, M.-S., Pinto, S. M., Getnet, D., Nirujogi, R. S., Manda, S. S., Chaerkady, R., Madugundu, A. K., Kelkar, D. S., Isserlin, R., Jain, S., et al. (2014) · Nature · doi:10.1038/nature13302 · Cited by 2,198 · see citing works
Deep learning for universal linear embeddings of nonlinear dynamics
Lusch, B., Kutz, J., & Brunton, S. (2017) · Nature Communications · doi:10.1038/s41467-018-07210-0 · Cited by 1,834 · see citing works
FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation
Yang, Y. L., Feng, C., Shen, Y., & Tian, D. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00029 · Cited by 1,365 · see citing works
Active liquid-like behavior of nucleoli determines their size and shape in Xenopus laevis oocytes
Brangwynne, C. P., Mitchison, T. J., & Hyman, A. A. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1017150108 · Cited by 1,285 · see citing works
The Human Protein Atlas—a tool for pathology
Pontén, F., Jirström, K., & Uhlén, M. (2008) · The Journal of Pathology · doi:10.1002/path.2440 · Cited by 1,126 · see citing works
The nucleolus as a multiphase liquid condensate
Lafontaine, D. L. J., Riback, J. A., Bascetin, R., & Brangwynne, C. P. (2020) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-020-0272-6 · Cited by 1,063 · see citing works
Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudsPreprint
Thomas, N., Smidt, T., Kearnes, S. M., Yang, L., Li, L., Kohlhoff, K., & Riley, P. F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.08219 · Cited by 726 · see citing works
Simpson, A. L., Antonelli, M., Bakas, S., Bilello, M., Farahani, K., van Ginneken, B., Kopp‐Schneider, A., Landman, B. A., Litjens, G. J. S., Menze, B. H., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.09063 · Cited by 703 · see citing works
Dynamics of DNA Replication Factories in Living Cells
Leonhardt, H., Rahn, H.-P., Weinzierl, P., Sporbert, A., Cremer, T. J., Zink, D., & Cardoso, M. C. (2000) · The Journal of Cell Biology · doi:10.1083/jcb.149.2.271 · Cited by 584 · see citing works
Multiplexed protein maps link subcellular organization to cellular states
Gut, G., Herrmann, M. D., & Pelkmans, L. (2018) · Science · doi:10.1126/science.aar7042 · Cited by 510 · see citing works
Nuclear speckles: molecular organization, biological function and role in disease
Gałgański, Ł., Urbanek-Trzeciak, M. O., & Krzyżosiak, W. J. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx759 · Cited by 489 · see citing works
MONAI: An open-source framework for deep learning in healthcarePreprint
Cardoso, M. J., Li, W., Brown, R., Ma, N., Kerfoot, E., Wang, Y., Murrey, B., Myronenko, A., Zhao, C. H., Yang, D., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.02701 · Cited by 485 · see citing works
Cutler, A., & Breiman, L. (1994) · Technometrics · doi:10.1080/00401706.1994.10485840 · Cited by 447 · see citing works
Laan, L., Pavin, N., Husson, J., Romet‐Lemonne, G., VanDuijn, M. M., López, M. P., Vale, R. D., Jülicher, F., Reck‐Peterson, S. L., & Dogterom, M. (2012) · Cell · doi:10.1016/j.cell.2012.01.007 · Cited by 396 · see citing works
Khater, I. M., Nabi, I. R., & Hamarneh, G. (2020) · Patterns · doi:10.1016/j.patter.2020.100038 · Cited by 288 · see citing works
Systematic gene tagging using CRISPR/Cas9 in human stem cells to illuminate cell organization
Roberts, B., Haupt, A., Tucker, A., Grancharova, T., Arakaki, J., Fuqua, M. A., Nelson, A., Hookway, C., Ludmann, S. A., Mueller, I. A., et al. (2017) · Molecular Biology of the Cell · doi:10.1091/mbc.e17-03-0209 · Cited by 262 · see citing works
Self-citationMarshall, W. F., Dernburg, A. F., Harmon, B., Agard, D. A., & Sedat, J. W. (1996) · Molecular Biology of the Cell · doi:10.1091/mbc.7.5.825 · Cited by 223 · see citing works
Vector Neurons: A General Framework for SO(3)-Equivariant Networks
Deng, C., Litany, O., Duan, Y., Poulenard, A., Tagliasacchi, A., & Guibas, L. J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01198 · Cited by 220 · see citing works
Masked Autoencoders Are Scalable Vision LearnersPreprint
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., & Girshick, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.06377 · Cited by 200 · see citing works
Coordinate-based colocalization analysis of single-molecule localization microscopy data
Malkusch, S., Endesfelder, U., Mondry, J., Gelléri, M., Verveer, P. J., & Heilemann, M. (2011) · Histochemistry and Cell Biology · doi:10.1007/s00418-011-0880-5 · Cited by 189 · see citing works
Chandrasekaran, S. N., Cimini, B. A., Goodale, A. B., Miller, L., Kost‐Alimova, M., Jamali, N., Doench, J. G., Fritchman, B., Skepner, A. P., Melanson, M., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02241-6 · Cited by 159 · see citing works
Cyclin-dependent kinases govern formation and maintenance of the nucleolus
Sirri, V., Hernandez‐Verdun, D., & Roussel, P. (2002) · The Journal of Cell Biology · doi:10.1083/jcb.200201024 · Cited by 147 · see citing works
cellPACK: A Virtual Mesoscope to Model and Visualize Structural Systems Biology
Johnson, G. T., Autin, L., Al-Alusi, M., Goodsell, D., Sanner, M. F., & Olson, A. (2014) · Nature Methods · doi:10.1038/nmeth.3204 · Cited by 140 · see citing works
Integrated intracellular organization and its variations in human iPS cells
Viana, M. P., Chen, J., Knijnenburg, T. A., Vasan, R., Yan, C., Arakaki, J. E., Bailey, M., Berry, B., Borensztejn, A., Brown, E. M., et al. (2023) · Nature · doi:10.1038/s41586-022-05563-7 · Cited by 132 · see citing works
Self-citationDiscovering governing equations from partial measurements with deep delay autoencoders
Bakarji, J., Champion, K., Kutz, J. N., & Brunton, S. L. (2023) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2023.0422 · Cited by 100 · see citing works
Cell Nuclei Spin in the Absence of Lamin B1
Ji, J. Y., Lee, R. T., Vergnes, L., Fong, L. G., Stewart, C. L., Reue, K., Young, S. G., Zhang, Q., Shanahan, C. M., & Lammerding, J. (2007) · Journal of Biological Chemistry · doi:10.1074/jbc.m611094200 · Cited by 99 · see citing works
Chen, J., Ding, L., Viana, M. P., Lee, H., Sluezwski, M. F., Morris, B., Hendershott, M. C., Yang, R., Mueller, I. A., & Rafelski, S. M. (2018) · doi:10.1101/491035 · Cited by 99 · see citing works
Self-citationMache, C., Schulze, J., Holland, G., Bourquain, D. R., Gensch, J.-M., Oh, D., Nitsche, A., Dürrwald, R., Laue, M. M., & Wolff, T. (2022) · Communications Biology · doi:10.1038/s42003-022-04068-3 · Cited by 94 · see citing works
Zaritsky, A., Jamieson, A. R., Welf, E. S., Nevarez, A. J., Cillay, J., Eskiocak, U., Cantarel, B. L., & Danuser, G. M. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.003 · Cited by 92 · see citing works
Establishing a conceptual framework for holistic cell states and state transitions
Rafelski, S. M., & Theriot, J. A. (2024) · Cell · doi:10.1016/j.cell.2024.04.035 · Cited by 92 · see citing works
Self-citationImplicit Autoencoder for Point-Cloud Self-Supervised Representation Learning
Yan, S.-M., Yang, Z., Li, H., Song, C., Guan, L., Kang, H., Hua, G., & Huang, Q.-X. (2022) · IEEE International Conference on Computer Vision · doi:10.1109/iccv51070.2023.01336 · Cited by 90 · see citing works
Equivariant Point Network for 3D Point Cloud Analysis
Chen, H., Liu, S., Chen, W., Li, H., & Hill, R. W. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01428 · Cited by 90 · see citing works
Anastassova-Kristeva, M. (1977) · Journal of Cell Science · doi:10.1242/jcs.25.1.103 · Cited by 82 · see citing works
Golgi Dynamics: The Morphology of the Mammalian Golgi Apparatus in Health and Disease
Makhoul, C., Gosavi, P., & Gleeson, P. A. (2019) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2019.00112 · Cited by 79 · see citing works
Boni, U. D., & Mintz, A. H. (1986) · Science · doi:10.1126/science.3775367 · Cited by 78 · see citing works
Robust induction of primordial germ cells of white rhinoceros on the brink of extinction
Hayashi, M., Zywitza, V., Naitou, Y., Hamazaki, N., Göritz, F., Hermes, R. E., Holtze, S., Lazzari, G., Galli, C., Stejskal, J., et al. (2022) · Science Advances · doi:10.1126/sciadv.abp9683 · Cited by 62 · see citing works
Evaluation of methods for generative modeling of cell and nuclear shape
Ruan, X., & Murphy, R. F. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty983 · Cited by 62 · see citing works
Müller, M.-C., Lemaire, O. N., Kurth, J. M., Welte, C. U., & Wagner, T. (2024) · Communications Biology · doi:10.1038/s42003-023-05726-w · Cited by 54 · see citing works
Golgi apparatus self-organizes into the characteristic shape via postmitotic reassembly dynamics
Tachikawa, M., & Mochizuki, A. (2017) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1619264114 · Cited by 47 · see citing works
Learning biophysical determinants of cell fate with deep neural networks
Soelistyo, C. J., Vallardi, G., Charras, G., & Lowe, A. R. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00503-6 · Cited by 44 · see citing works
A deep generative model of 3D single-cell organization
Donovan-Maiye, R. M., Brown, J. M., Chan, C. K., Ding, L., Yan, C., Gaudreault, N., Theriot, J. A., Maleckar, M. M., Knijnenburg, T. A., & Johnson, G. R. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009155 · Cited by 44 · see citing works
Self-citationHaas, K. T., Lee, M.-Y., Esposito, A., & Venkitaraman, A. R. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1303 · Cited by 35 · see citing works
Positioning centrioles and centrosomes
Hannaford, M. R., & Rusan, N. M. (2024) · The Journal of Cell Biology · doi:10.1083/jcb.202311140 · Cited by 34 · see citing works
Burgess, J., Nirschl, J., Zanellati, M. C., Lozano, A., Cohen, S., & Yeung, S. Y. (2024) · Nature Communications · doi:10.1038/s41467-024-45362-4 · Cited by 31 · see citing works
Cytoskeletal rearrangement precedes nucleolar remodeling during adipogenesis
Potolitsyna, E., Pickering, S. H., Bellanger, A., Germier, T., Collas, P., & Briand, N. (2024) · Communications Biology · doi:10.1038/s42003-024-06153-1 · Cited by 24 · see citing works
Gregor, B. W., Coston, M. E., Adams, E. M., Arakaki, J., Borensztejn, A., Do, T. P., Fuqua, M. A., Haupt, A., Hendershott, M. C., Leung, W. W., et al. (2023) · Nature Protocols · doi:10.1038/s41596-023-00912-w · Cited by 17 · see citing works
Self-citationImproving and evaluating deep learning models of cellular organization
Sun, H., Fu, X., Abraham, S., Shen, J., & Murphy, R. F. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac688 · Cited by 16 · see citing works
3D single-cell shape analysis using geometric deep learningPreprint
Vries, M. D., Dent, L., Curry, N., Rowe-Brown, L., Bousgouni, V., Tyson, A. L., Dunsby, C. W., & Bakal, C. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.17.496550 · Cited by 11 · see citing works
Discovering interpretable models of scientific image data with deep learning
Soelistyo, C. J., & Lowe, A. R. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00682 · Cited by 10 · see citing works
Unpaired Multi-Domain Causal Representation Learning
Sturma, N., Squires, C., Drton, M., & Uhler, C. (2023) · neural information processing systems · doi:10.52202/075280-1496 · Cited by 10 · see citing works
Archetypal analysis as an autoencoder
Bauckhage, C., Kristian Kersting, B., Hoppe, F., & Thurau, C. (2015) · Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) · Cited by 10 · see citing works
Learning dynamic image representations for self-supervised cell cycle annotationPreprint
Ulicna, K., Kelkar, M., Soelistyo, C. J., Charras, G., & Lowe, A. R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.30.542796 · Cited by 10 · see citing works
Unsupervised Learning of Group Invariant and Equivariant Representations
Winter, R., Bertolini, M., Le, T. A., Noé, F., & Clevert, D.-A. (2022) · neural information processing systems · doi:10.52202/068431-2315 · Cited by 8 · see citing works
Rotem, O., Schwartz, T., Maor, R., Tauber, Y., Shapiro, M. T., Meseguer, M., Gilboa, D., Seidman, D. S., & Zaritsky, A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.15.566968 · Cited by 5 · see citing works
Zharskaya, O. O., Barsukova, A. S., & Zatsepina, O. V. (2008) · Biochemistry (Moscow) · doi:10.1134/s0006297908040056 · Cited by 3 · see citing works
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1 of the 70 references couldn’t be matched to a known paper.
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Oh, S., & Wijaya, J. (2026) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2026.109665 · Cited by 2 · see citing works
Deep learning of functional perturbations from condensate morphology
Donlic, A., Comi, T. J., Quinodoz, S. A., Jaberi-Lashkari, N., Antunes, K. H., Jiang, L., Wiesner, L. W., Lim, A. I., & Brangwynne, C. P. (2026) · Cell · doi:10.1016/j.cell.2026.05.010 · Cited by 1 · see citing works
Decoding 3D cell morphology with interpretable point cloud models
Hugelier, S. (2025) · Nature Methods · doi:10.1038/s41592-025-02752-w
Oh, S., Wijaya, J., Nguyen, H. T., Lee, J. E., & Yoon, J.-K. (2025) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2025.109318
DeepSRFusion: a point cloud deep learning framework for super-resolution particle fusionPreprint
Qiao, Y., Wang, J., Xi, J., Ding, J., Chen, T., Zhang, Y., Qiu, L., Zhao, W., Liu, J., & Xu, F. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.26.708405
MorphCell learns interpretable shape representations of 3D cellular morphologyPreprint
Zhang, G., Wang, G., Cao, R., Guo, J., Xu, S., Yu, Z., Zheng, Y., He, Y., & Feng, X. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.17.752374
Geometry‐aware modeling with cellPACK for evaluating spatial trends in intracellular organization
Mogre, S. S., Li, R., Scibisz, A., Thảo, Đ. T., Dixon, J. C., Viana, M. P., Nazockdast, E., Rafelski, S. M., Theriot, J. A., Riel-Mehan, M. M., et al. (2026) · Protein Science · doi:10.1002/pro.70832
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- Authors
- Vasan, R., Ferrante, A. J., Borensztejn, A., Frick, C. L., Garrison, P., Gaudreault, N., Mogre, S. S., Mohammed, F. S., Morris, B., Pires, G. G., Saelid, D., Rafelski, S. M., Theriot, J. A., & Viana, M. P.
- Affiliations
- Allen Institute for Cell Science (US)
- Altius Institute for Biomedical Sciences (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Viana, Matheus P.
- ORCID
- Venue
- Nature Methods
- Published
- 2025-07
- Volume
- 22
- Issue
- 7
- Pages
- 1531-1544
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC12240800
- OpenAlex
- W4412008145
- Cited by
- 7
- References
- 70
- Citations by year
- 2026: 5 · 2025: 2
- FWCI
- 2.64
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Methods (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health
- University of California, San Francisco
- National Human Genome Research Institute (UM1HG011593)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- representation learning, point clouds, intracellular structures, subcellular organization, DNA replication foci, nucleolus, unsupervised clustering, phenotypic profiling, drug-induced perturbation, 3D shape representation
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