The Specious Art of Single-Cell Genomics
Open accessAbstract
Dimensionality reduction is standard practice for filtering noise and identifying relevant features in large-scale data analyses. In biology, single-cell genomics studies typically begin with reduction to two or three dimensions to produce ‘all-in-one’ visuals of the data that are amenable to the human eye, and these are subsequently used for qualitative and quantitative exploratory analysis. However, there is little theoretical support for this practice, and we show that extreme dimension reduction, from hundreds or thousands of dimensions to two, inevitably induces significant distortion of high-dimensional datasets. We therefore examine the practical implications of low-dimensional embedding of single-cell data, and find that extensive distortions and inconsistent practices make such embeddings counter-productive for exploratory, biological analyses. In lieu of this, we discuss alternative approaches for conducting targeted embedding and feature exploration, to enable hypothesis-driven biological discovery.
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Relying only on 2D embeddings can lead to misinterpretation of relationships between cells.
“a recent study showed that relying only on 2D embeddings can lead to misinterpretation of the relationships between cells”
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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
“Independent benchmarks 5,50,51 showed that community detection based on graph modularity optimization via the Louvain algorithm works best for cluster identification.”
MethodMarsh, S. E., Walker, A. J., Kamath, T., Dissing-Olesen, L., Hammond, T. R., de Soysa, T. Y., Young, A. M. H., Murphy, S., Abdulraouf, A., Nadaf, N., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01022-8 · Cited by 432 · see citing works
Elhaik, E. (2022) · Scientific Reports · doi:10.1038/s41598-022-14395-4 · Cited by 318 · see citing works
A single-cell time-lapse of mouse prenatal development from gastrula to birth
Qiu, C., Martin, B., Welsh, I. C., Daza, R. M., Le, T.-M., Huang, X., Nichols, E. K., Taylor, M. L., Fulton, O., O’Day, D. R., et al. (2024) · Nature · doi:10.1038/s41586-024-07069-w · Cited by 199 · see citing works
Gorin, G., Fang, M., Chari, T., & Pachter, L. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010492 · Cited by 186 · see citing works
Self-citationA fast, scalable and versatile tool for analysis of single-cell omics data
Zhang, K., Zemke, N. R., Armand, E. J., & Ren, B. (2024) · Nature Methods · doi:10.1038/s41592-023-02139-9 · Cited by 185 · see citing works
Gatto, L., Aebersold, R. H., Cox, J., Demichev, V., Derks, J., Emmott, E., Franks, A. M., Ivanov, A. R., Kelly, R., Khoury, L., et al. (2023) · Nature Methods · doi:10.1038/s41592-023-01785-3 · Cited by 180 · see citing works
The benefits and pitfalls of machine learning for biomarker discovery
Ng, S. L.-K., Masarone, S., Watson, D., & Barnes, M. R. (2023) · Cell and Tissue Research · doi:10.1007/s00441-023-03816-z · Cited by 181 · see citing works
Mapping cells through time and space with moscot
Klein, D., Palla, G., Lange, M., Klein, M., Piran, Z., Gander, M., Meng-Papaxanthos, L., Sterr, M., Saber, L., Jing, C., et al. (2025) · Nature · doi:10.1038/s41586-024-08453-2 · Cited by 202 · see citing works
Single-cell transcriptomic and spatial landscapes of the developing human pancreas
Olaniru, O. E., Kadolsky, U. D., Kannambath, S., Vaikkinen, H., Fung, K., Dhami, P., & Persaud, S. J. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.11.009 · Cited by 138 · see citing works
Faure, L., Soldatov, R., Kharchenko, P. V., & Adameyko, I. I. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac746 · Cited by 113 · see citing works
Liu, W., Liao, X., Yang, Y., Lin, H., Yeong, J. P.-S., Zhou, X., Shi, X., & Liu, J. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac219 · Cited by 89 · 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
Spatial transcriptomic clocks reveal cell proximity effects in brain ageing
Sun, E., Zhou, O. Y., Hauptschein, M., Rappoport, N., Xu, L., Negredo, P. N., Liu, L. A., Rando, T. A., Zou, J., & Brunet, A. (2024) · Nature · doi:10.1038/s41586-024-08334-8 · Cited by 98 · see citing works
Evaluating batch correction methods for image-based cell profiling
Arevalo, J., Su, E., van Dijk, R., Carpenter, A. E., & Singh, S. (2023) · doi:10.1101/2023.09.15.558001 · Cited by 89 · see citing works
Evaluating batch correction methods for image-based cell profiling
Arévalo, J., Su, E., Ewald, J., van Dijk, R., Carpenter, A. E., & Singh, S. (2024) · Nature Communications · doi:10.1038/s41467-024-50613-5 · Cited by 88 · see citing works
Seyfferth, C., Eekhout, T., Shi, D., Neumann, M., Berg, L. S., Ke, Y., Shahan, R., Cox, K. L., Gómez-Cano, F., Nelissen, H., et al. (2024) · The Plant Cell · doi:10.1093/plcell/koae003 · Cited by 82 · see citing works
Topological turning points across the human lifespan
Mousley, A., Bethlehem, R. A. I., Yeh, F.-C. F., & Astle, D. E. (2025) · Nature Communications · doi:10.1038/s41467-025-65974-8 · Cited by 91 · see citing works
Chen, K., Zhou, Y., Ding, M., Wang, Y., Ren, Z., & Yang, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae163 · Cited by 77 · see citing works
Xia, L., Lee, C., & Li, J. J. (2024) · Nature Communications · doi:10.1038/s41467-024-45891-y · Cited by 69 · see citing works
Bhakat, S. (2021) · RSC Advances · doi:10.1039/d2ra03660f · Cited by 42 · see citing works
Rastermap: a discovery method for neural population recordings
Stringer, C., Zhong, L., Syeda, A., Du, F., Kesa, M., & Pachitariu, M. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01783-4 · Cited by 68 · see citing works
Durán, R. C., González-Orozco, J. C., Velasco, I., & Wu, J. Q. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.884748 · Cited by 64 · see citing works
Zheng, L., Shi, S., Lu, M., Fang, P., Pan, Z., Zhang, H., Zhou, Z., Zhang, H., Mou, M., Huang, S., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03166-1 · Cited by 67 · see citing works
Seeing data as t-SNE and UMAP do
Marx, V. (2024) · Nature Methods · doi:10.1038/s41592-024-02301-x · Cited by 67 · see citing works
Coverage bias in small molecule machine learning
Kretschmer, F., Seipp, J., Ludwig, M., Klau, G. W., & Böcker, S. (2025) · Nature Communications · doi:10.1038/s41467-024-55462-w · Cited by 76 · see citing works
Lange, M., Granados, A. A., VijayKumar, S., Bragantini, J., Ancheta, S., Santhosh, S., Borja, M., Kobayashi, H., McGeever, A., Solak, A. C., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.06.531398 · Cited by 66 · see citing works
Considerations for building and using integrated single-cell atlases
Hrovatin, K., Sikkema, L., Shitov, V. A., Heimberg, G., Shulman, M., Oliver, A. J., Mueller, M. F., Ibarra, I. L., Wang, H., Ramírez-Suástegui, C., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02532-y · Cited by 67 · see citing works
Neural manifold analysis of brain circuit dynamics in health and disease
Mitchell‐Heggs, R., Prado, S. V., Gava, G. P., Go, M. A., & Schultz, S. R. (2022) · Journal of Computational Neuroscience · doi:10.1007/s10827-022-00839-3 · Cited by 61 · see citing works
Visualizing metagenomic and metatranscriptomic data: A comprehensive review
Aplakidou, E., Vergoulidis, N., Chasapi, M. N., Venetsianou, N. K., Kokoli, M., Panagiotopoulou, E., Iliopoulos, I., Karatzas, E., Pafilis, E., Georgakopoulos‐Soares, I., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.04.060 · Cited by 62 · see citing works
Dynamic visualization of high-dimensional data
Sun, E., Ma, R., & Zou, J. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00380-4 · Cited by 58 · see citing works
Phantom oscillations in principal component analysis
Shinn, M. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311420120 · Cited by 61 · see citing works
Case-control analysis of single-cell RNA-seq studiesPreprint
Petukhov, V., Igolkina, A. A., Rydbirk, R., Mei, S., Christoffersen, L., Khodosevich, K., & Kharchenko, P. V. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.15.484475 · Cited by 51 · see citing works
Are batch effects still relevant in the age of big data?
Goh, W. W. B., Yong, C. H., & Wong, L. (2022) · Trends in biotechnology · doi:10.1016/j.tibtech.2022.02.005 · Cited by 54 · see citing works
Technology meets TILs: Deciphering T cell function in the -omics era
Hudson, W. H., & Wieland, A. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.011 · Cited by 53 · see citing works
Mapping cells through time and space with moscotPreprint
Klein, D., Palla, G., Lange, M., Klein, M., Piran, Z., Gander, M., Meng-Papaxanthos, L., Sterr, M., Bastidas-Ponce, A., Tarquis-Medina, M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.11.540374 · Cited by 49 · see citing works
A comprehensive review of spatial transcriptomics data alignment and integration
Khan, M. A., Arslanturk, S., & Drăghici, S. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf536 · Cited by 55 · see citing works
RNAincoder: a deep learning-based encoder for RNA and RNA-associated interaction
Wang, Y., Chen, Z., Pan, Z., Huang, S., Liu, J., Xia, W., Zhang, H., Zheng, M., Li, H., Hou, T., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad404 · Cited by 46 · see citing works
Origin and segregation of the human germline
Castillo-Venzor, A., Penfold, C. A., Morgan, M. D., Tang, W. W. C., Kobayashi, T., Wong, F. C. K., Bergmann, S., Slatery, E. M., Boroviak, T., Marioni, J. C., et al. (2023) · Life Science Alliance · doi:10.26508/lsa.202201706 · Cited by 42 · see citing works
Inference of high-resolution trajectories in single-cell RNA-seq data by using RNA velocity
Zhang, Z., & Zhang, X. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100095 · Cited by 42 · see citing works
A persistent behavioral state enables sustained predation of humans by mosquitoes
Sorrells, T. R., Pandey, A., Rosas-Villegas, A., & Vosshall, L. B. (2022) · eLife · doi:10.7554/elife.76663 · Cited by 40 · see citing works
Neural ADMIXTURE for rapid genomic clustering
Mantes, A. D., Montserrat, D. M., Bustamante, C. D., Giró-i-Nieto, X., & Ioannidis, A. G. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00482-7 · Cited by 43 · see citing works
Lost in space: what single-cell RNA sequencing cannot tell you
Adema, K., Schon, M. A., Nodine, M. D., & Kohlen, W. (2024) · Trends in Plant Science · doi:10.1016/j.tplants.2024.03.010 · Cited by 42 · see citing works
Normalization for sampled count data
Booeshaghi, A. S., Hallgrímsdóttir, I. B., Gálvez-Merchán, Á., & Pachter, L. (2022) · doi:10.1101/2022.05.06.490859 · Cited by 39 · see citing works
Self-citationMulè, M. P., Martins, A. J., Cheung, F., Farmer, R., Sellers, B. A., Quiel, J. A., Jain, A., Kotliarov, Y., Bansal, N., Chen, J., et al. (2024) · Immunity · doi:10.1016/j.immuni.2024.04.009 · Cited by 43 · see citing works
Studying stochastic systems biology of the cell with single-cell genomics data
Gorin, G., Vastola, J. J., & Pachter, L. S. (2023) · Cell Systems · doi:10.1016/j.cels.2023.08.004 · Cited by 38 · see citing works
Self-citationThe art of seeing the elephant in the room: 2D embeddings of single-cell data do make sense
Lause, J., Kobak, D., & Berens, P. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012403 · Cited by 31 · see citing works
A Guide to Trajectory Inference and RNA Velocity
Weiler, P., den Berge, K. V., Street, K., & Tiberi, S. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2756-3_14 · Cited by 33 · see citing works
Single-Cell RNA Sequencing for Studying Human Cancers
Aran, D. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020722-091857 · Cited by 31 · see citing works
Dissociable brain structural asymmetry patterns reveal unique phenome-wide profiles
Saltoun, K., Adolphs, R., Paul, L. K., Sharma, V., Diedrichsen, J., Yeo, B. T. T., & Bzdok, D. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01461-0 · Cited by 30 · see citing works
Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq
Stokes, T., Cen, H. H., Kapranov, P., Gallagher, I. J., Pitsillides, A. A., Volmar, C., Kraus, W. E., Johnson, J. D., Phillips, S. M., Wahlestedt, C., et al. (2023) · Advanced Genetics · doi:10.1002/ggn2.202200024 · Cited by 26 · see citing works
Lederer, A. R., Leonardi, M., Talamanca, L., Bobrovskiy, D. M., Herrera, A., Droin, C., Khven, I. M., Carvalho, H. J. F., Valente, A., Mantes, A. D., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02471-8 · Cited by 32 · see citing works
A spectrum of explainable and interpretable machine learning approaches for genomic studies
Conard, A. M., DenAdel, A., & Crawford, L. A. (2023) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1617 · Cited by 30 · see citing works
Carbonetto, P. S., Luo, K., Sarkar, A., Hung, A., Tayeb, K., Pott, S., & Stephens, M. (2023) · Genome biology · doi:10.1186/s13059-023-03067-9 · Cited by 26 · see citing works
Multidimensional political polarization in online social networks
Peralta, A. F., Morales, P. R., Kertész, J., & Íñiguez, G. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.013170 · Cited by 26 · see citing works
Sivaraman, V., Wu, Y., & Perer, A. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511137 · Cited by 27 · see citing works
On the Versatile Uses of Partial Distance Correlation in Deep Learning
Zhen, X., Meng, Z., Chakraborty, R., & Singh, V. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19809-0_19 · Cited by 26 · see citing works
Stem-cell states converge in multistage cutaneous squamous cell carcinoma development
Taylor, M. A., Kandyba, E. E., Halliwill, K. D., Delrosario, R., Khoroshkin, M., Goodarzi, H., Quigley, D. A., Li, Y. R., Wu, D., Bollam, S., et al. (2024) · Science · doi:10.1126/science.adi7453 · Cited by 27 · see citing works
Molecular causality in the advent of foundation models
Lobentanzer, S., Rodríguez-Mier, P., Bauer, S., & Sáez-Rodríguez, J. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00041-w · Cited by 23 · see citing works
Uncovering 2-D toroidal representations in grid cell ensemble activity during 1-D behavior
Hermansen, E., Klindt, D. A., & Dunn, B. A. (2024) · Nature Communications · doi:10.1038/s41467-024-49703-1 · Cited by 22 · see citing works
Jiang, J., Chen, S., Tsou, T., McGinnis, C. S., Khazaei, T., Zhu, Q., Park, J. H., Strazhnik, I.-M., Vielmetter, J., Gong, Y., et al. (2023) · doi:10.1101/2023.04.19.537364 · Cited by 21 · see citing works
Principled and interpretable alignability testing and integration of single-cell data
Ma, R., Sun, E., Donoho, D. L., & Zou, J. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2313719121 · Cited by 21 · see citing works
Watson, E. R., Mora, A., Fard, A. T., & Mar, J. C. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac387 · Cited by 20 · see citing works
Mapping oto-pharyngeal development in a human inner ear organoid model
Steinhart, M. R., van der Valk, W. H., Osorio, D., Serdy, S. A., Zhang, J., Nist-Lund, C. A., Kim, J., Moncada-Reid, C., Sun, L., Lee, J., et al. (2023) · Development · doi:10.1242/dev.201871 · Cited by 23 · see citing works
Erasure of Biologically Meaningful Signal by Unsupervised scRNAseq Batch-correction MethodsPreprint
Tyler, S. R., Guccione, E., & Schadt, E. E. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.15.468733 · Cited by 20 · see citing works
Mendieta, J. P., Sangra, A., Yan, H., Minow, M. A. A., & Schmitz, R. J. (2023) · The Plant Journal · doi:10.1111/tpj.16351 · Cited by 20 · see citing works
Geometric deep learning and multiple-instance learning for 3D cell-shape profiling
Vries, M. D., Dent, L., Curry, N., Rowe-Brown, L., Bousgouni, V., Fourkioti, O., Naidoo, R., Sparks, H., Tyson, A. L., Dunsby, C. W., et al. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101229 · Cited by 21 · see citing works
Joint representation and visualization of derailed cell states with Decipher
Nazaret, A., Fan, J. L., Lavallée, V., Burdziak, C., Cornish, A. E., Kiseliovas, V., Bowman, R. L., Masilionis, I., Chun, J., Eisman, S. E., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03682-8 · Cited by 18 · see citing works
Defining breast epithelial cell types in the single-cell era
Gray, G. K., Carlson, E. G., Lev, T., Marshall, B., Reed, A. D., Covarrubias, A. P. S., Twigger, A., Puig-Barbe, A., Thennavan, A., Omotoso, A. J., et al. (2025) · Developmental Cell · doi:10.1016/j.devcel.2025.06.032 · Cited by 20 · see citing works
iSegMSI: An Interactive Strategy to Improve Spatial Segmentation of Mass Spectrometry Imaging Data
Guo, L., Liu, X., Zhao, C., Hu, Z., Xu, X., Cheng, K., Zhou, P., Xiao, Y., Shah, M., Xu, J., et al. (2022) · Analytical Chemistry · doi:10.1021/acs.analchem.2c01456 · Cited by 19 · see citing works
What cannot be seen correctly in 2D visualizations of single-cell ‘omics data?
Wang, S., Sontag, E. D., & Lauffenburger, D. A. (2023) · Cell Systems · doi:10.1016/j.cels.2023.07.002 · Cited by 19 · see citing works
Biophysically interpretable inference of cell types from multimodal sequencing data
Chari, T., Gorin, G., & Pachter, L. S. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00689-2 · Cited by 20 · see citing works
Self-citationAllometry of cell types in planarians by single-cell transcriptomics
Emili, E., Pérez-Posada, A., Vanni, V., Salamanca-Díaz, D. A., Ródriguez-Fernández, D., Christodoulou, M. D., & Solana, J. (2025) · Science Advances · doi:10.1126/sciadv.adm7042 · Cited by 19 · see citing works
Soft Modes as a Predictive Framework for Low-Dimensional Biological Systems Across Scales
Russo, C. J., Husain, K. B., & Murugan, A. (2025) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-081624-030543 · Cited by 22 · see citing works
Intrapancreatic adipocytes and beta cell dedifferentiation in human type 2 diabetes
Zhang, N., Sun, Q., Zhang, J., Zhang, R., Liu, S., Zhao, X., Ma, J., & Li, X. (2025) · Diabetologia · doi:10.1007/s00125-025-06392-9 · Cited by 18 · see citing works
The role of single-cell genomics in human genetics
Sreenivasan, V. K. A., Balachandran, S., & Spielmann, M. (2022) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2022-108588 · Cited by 18 · see citing works
Padrón-Manrique, C., Vázquez-Jiménez, A., Esquivel-Hernandez, D. A., Martínez-López, Y. E., Neri-Rosario, D., Giron-Villalobos, D., Mixcoha, E., Paul, S.-C. J., & Reséndis-Antonio, O. (2024) · Biology · doi:10.3390/biology13070512 · Cited by 23 · see citing works
Marine Invertebrates One Cell at A Time: Insights from Single-Cell Analysis
Bump, P. A., & Lubeck, L. (2023) · Integrative and Comparative Biology · doi:10.1093/icb/icad034 · Cited by 17 · see citing works
Falconnier, C., Caparros-Roissard, A., Decraene, C., & Lutz, P.-É. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02238-1 · Cited by 18 · see citing works
Bitter peptide prediction using graph neural networks
Srivastava, P., Steuer, A., Ferri, F., Nicoli, A., Schultz, K., Bej, S., Pizio, A. D., & Wolkenhauer, O. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00909-x · Cited by 19 · see citing works
Representing the dynamics of natural marmoset vocal behaviors in frontal cortex
Li, J., Aoi, M., & Miller, C. T. (2024) · Neuron · doi:10.1016/j.neuron.2024.08.020 · Cited by 18 · see citing works
Gorin, G., Chari, T., Carilli, M., Vastola, J. J., & Pachter, L. S. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.11.495771 · Cited by 16 · see citing works
Self-citationGene regulatory networks: from correlative models to causal explanations
Maizels, R. J., & Briscoe, J. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-026-00939-1 · Cited by 18 · see citing works
scTOP: physics-inspired order parameters for cellular identification and visualization
Yampolskaya, M., Herriges, M. J., Ikonomou, L., Kotton, D. N., & Mehta, P. (2023) · Development · doi:10.1242/dev.201873 · Cited by 15 · see citing works
Minot, S. S., Garb, B. F., Roldan, A., Tang, A., Oskotsky, T., Rosenthal, C. A., Hoffman, N. G., Sirota, M., & Golob, J. L. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100639 · Cited by 15 · see citing works
Identifying cell states in single-cell RNA-seq data at statistically maximal resolution
Grobecker, P., Sakoparnig, T., & van Nimwegen, E. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012224 · Cited by 15 · see citing works
Waddington landscape for prototype learning in generalized Hopfield networks
Boukacem, N. E., Leary, A. Y., Thériault, R., Gottlieb, F., Mani, M., & François, P. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.033098 · Cited by 15 · see citing works
Hansen, T. F., & Aarset, A. (2024) · Rock Mechanics and Rock Engineering · doi:10.1007/s00603-024-04280-z · Cited by 15 · see citing works
Topological stratification of continuous genetic variation in large biobanks
Diaz-Papkovich, A., Zabad, S., Snell, H., Ben-Eghan, C., Anderson-Trocmé, L., Femerling, G., Nathan, V., Patel, J., & Gravel, S. (2026) · PLoS Genetics · doi:10.1371/journal.pgen.1012068 · Cited by 15 · see citing works
Bienkowski, M. S. (2023) · Hippocampus · doi:10.1002/hipo.23508 · Cited by 16 · see citing works
Craniofacial developmental biology in the single-cell era
Tseng, K.-C., & Crump, J. G. (2023) · Development · doi:10.1242/dev.202077 · Cited by 14 · see citing works
Compound-SNE: comparative alignment of t-SNEs for multiple single-cell omics data visualization
Cess, C. G., & Haghverdi, L. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae471 · Cited by 14 · see citing works
A thousand and one tumors: the promise of AI for cancer biology
Fan, J. L., Nazaret, A., & Azizi, E. (2024) · Nature Methods · doi:10.1038/s41592-024-02364-w · Cited by 14 · see citing works
Vardaman, D., Ali, M. A., Siam, M. H. B., Bolding, C., Tidwell, H., Stephens, H. R., Patil, M. N., & Tyrrell, D. J. (2024) · The Journal of Immunology · doi:10.4049/jimmunol.2400370 · Cited by 14 · see citing works
Mocking, T. R., Kelder, A., Reuvekamp, T., Ngai, L. L., Rutten, P., Gradowska, P. L., van de Loosdrecht, A. A., Cloos, J. J., & Bachas, C. (2024) · Communications Medicine · doi:10.1038/s43856-024-00700-x · Cited by 14 · see citing works
Assessing and improving reliability of neighbor embedding methods: a map-continuity perspective
Liu, Z., Ma, R., & Zhong, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-60434-9 · Cited by 13 · see citing works
Badia-i-Mompel, P., Casals-Franch, R., Wessels, L., Müller‐Dott, S., Trimbour, R., Yang, Y., Flores, R. O. R., & Sáez-Rodríguez, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.20.629764 · Cited by 14 · see citing works
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Considerations in Pursuing Reaction Scope Generality
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- Authors
- Chari, T., & Pachter, L.
- Affiliations
- California Institute of Technology (US)
- Corresponding author
- Pachter, Lior
- Venue
- PLoS Computational Biology
- Publisher
- openRxiv
- Published
- 2021-08-26
- Volume
- 19
- Issue
- 8
- Pages
- e1011288-e1011288
- ISSN
- 1553-734X (ISSN-L)
- PMC
- PMC10434946
- OpenAlex
- W4385945347
- Cited by
- 379
- References
- 93
- Citations by year
- 2026: 73 · 2025: 97 · 2024: 102 · 2023: 63 · 2022: 40 · 2021: 3
- FWCI
- 29.85
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion, cc-by-nc)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- California Institute of Technology
- Resnick Sustainability Institute for Science, Energy and Sustainability, California Institute of Technology
- National Institutes of Health
- National Institute of Mental Health (U19MH114830)
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Keywords
- single-cell genomics, dimensionality reduction, embedding distortion, high-dimensional data, exploratory data analysis
- MeSH terms
- Data Analysis, Humans, Genomics
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