The DOI matches its Crossref record; 96 of the 96 references it cites were found in a registry or an index.
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.
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?
In the citing paper
“a recent study showed that relying only on 2D embeddings can lead to misinterpretation of the relationships between cells”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [49]
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
(2008) · Journal of Machine Learning Research · Cited by 33,051
Integrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508
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
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
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
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
The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]
Deng, L. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2211477 · Cited by 5,401
Dimensionality reduction for visualizing single-cell data using UMAP
Becht, E., McInnes, L., Healy, J., Dutertre, C.-A., Kwok, I. W. H., Ng, L. G., Ginhoux, F., & Newell, E. W. (2019) · Nature Biotechnology · doi:10.1038/nbt.4314 · Cited by 5,358
Qgraph: Network visualizations of relationships in psychometric data
Epskamp, S., Cramer, A., Waldorp, L., Schmittmann, V., & Borsboom, D. (2012) · Journal of Statistical Software · doi:10.18637/jss.v048.i04 · Cited by 4,844
Flagged first, then by citations
Research works printed but not linked: 4
- 2005, Approximation algorithms for low-distortion embeddings into low-dimensional spaces in SODA, 5, 119
- Tukey, J. W. Exploratory data analysis as part of a larger whole in Proceedings of the 18th conference on design of experiments in army research and development i. Washington, dc 1010 (apps.dtic.mil, 1972).
- Goldberger, J. , Roweis, S. , Hinton, G. & Salakhutdinov, R. Neighbourhood components analysis in Proceedings of the 17th International Conference on Neural Information Processing Systems (MIT Press, Vancouver, British Columbia, Canada, Dec. 2004), 513–520.
- 2008, SplitsTree 4.0-Computation of phylogenetic trees and networks, Bioinformatics, 14, 68
Cited by
Sorted by citations
Items 201–300 of 378
A supervised take on dimensionality reduction via hybrid subset selection
Rahimikollu, J., & Das, J. (2022) · Patterns · doi:10.1016/j.patter.2022.100563 · Cited by 3
On Manifold Learning in Plato’s Cave: Remarks on Manifold Learning and Physical Phenomena
Lederman, R. R., & Toader, B. (2023) · International Conference on Sampling Theory and Applications · doi:10.1109/sampta59647.2023.10301403 · Cited by 3
ScatterUQ: Interactive Uncertainty Visualizations for Multiclass Deep Learning Problems
Li, H. X., Jorgensen, S., Holodnak, J. T., & Wollaber, A. (2023) · Visualization · doi:10.1109/vis54172.2023.00058 · Cited by 3
Systems biology of B cells in COVID-19
Woodruff, M. C., Faliti, C. E., & Sanz, I. (2024) · Seminars in Immunology · doi:10.1016/j.smim.2024.101875 · Cited by 3
Vo, H. K., Dawes, J., & Kelsh, R. N. (2024) · Journal of The Royal Society Interface · doi:10.1098/rsif.2023.0537 · Cited by 3
Uno, M., Nono, M., Takahashi, C., Kishimoto, S., Okabe, E., Yamamoto, T., & Nishida, E. (2025) · Genes to Cells · doi:10.1111/gtc.13187 · Cited by 3
Vega, C., Kratochvíl, M., Satagopam, V., & Schneider, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-07802-6_30 · Cited by 3
Dimensionality reduction and statistical modeling of scGET-seq dataPreprint
de Pretis, S., & Cittaro, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.29.498092 · Cited by 3
Extracting extended vocal units from two neighborhoods in the embedding planePreprint
Lorenz, C., Hao, X., Tomka, T., Rüttimann, L., & Hahnloser, R. H. R. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.26.509501 · Cited by 3
Towards the Transcriptionally based Classification of L6b in the Adult Mouse Brain
Henning, T., Bandiera, S., & Molnár, Z. (2023) · Wiley eBooks · doi:10.1002/9781119860914.ch16 · Cited by 3
Visualizing single-cell data with the neighbor embedding spectrumPreprint
Damrich, S., Klockow, M. V., Berens, P., Hamprecht, F. A., & Kobak, D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.26.590867 · Cited by 3
Yarıcı, M., & Karabekmez, M. E. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.21.619465 · Cited by 3
de Groot, D. H., Leonardo, S. X. M., Pachkov, M. V., & van Nimwegen, E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.08.652944 · Cited by 3
Stress-adapted codon usage enables efficient virulence-gene expression in SalmonellaPreprint
Goormaghtigh, F., & Bumann, D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.22.707322 · Cited by 6
TopoMetry systematically learns and evaluates the latent geometry of single-cell dataPreprint
Sidarta-Oliveira, D., Domingos, A. I., & Velloso, L. A. (2022) · eLife · doi:10.1101/2022.03.14.484134 · Cited by 3
Asloudj, Y., Fleur Mougin, A., & Thébault, P. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf073 · Cited by 2
Securing diagonal integration of multimodal single-cell data against ambiguous mapping
Zhou, H., Cao, K., & Lu, Y. Y. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf345 · Cited by 2
Larval zebrafish swim bouts in three dimensions reveal both new and redundant behaviours
Ravan, A., Chemla, Y. R., & Gruebele, M. (2025) · Journal of The Royal Society Interface · doi:10.1098/rsif.2025.0065 · Cited by 2
SuperGLUE facilitates an explainable training framework for multi-modal data analysis
Liu, T., Zhao, J., & Zhao, H. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101167 · Cited by 2
Mullan, K. A., Valkiers, S., de Vrij, N., Li, C., Verbandt, S., Pu, T., & Meysman, P. (2025) · ImmunoInformatics · doi:10.1016/j.immuno.2025.100063 · Cited by 2
A novel paradigm for single-cell annotation in stem cell research
Atkeson, T., Wilson, S. B., Ruiz-Morales, E. R., Little, M. H., Vento‐Tormo, R., Neavin, D. R., & Powell, J. E. (2025) · Stem Cell Reports · doi:10.1016/j.stemcr.2025.102707 · Cited by 3
Arabshahi, S., Karsalari, P. D., Baghsheikhi, H., Mogharrabi, F., Samieefar, N., & Rezaei, N. (2026) · Advances in Biomarker Sciences and Technology · doi:10.1016/j.abst.2026.01.005 · Cited by 2
Synergizing Chemical and AI Communities for Advancing Laboratories of the Future
Oh, S., Fang, X., Lin, I.-H., Dee, P., Dunham, C. S., Copp, S. M., Doyle, A. G., de Alaniz, J. R., & Gu, M. (2026) · ACS Central Science · doi:10.1021/acscentsci.5c01994 · Cited by 5
Single-cell aging clocks: A precision tool for dissecting and targeting the aging process
Hao, Y., Xie, S., Wei, Y., Sun, X., Xiao, Y., Xie, X., Han, K., Wang, T., Wang, Z., Zhang, L., et al. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103040 · Cited by 2
Chicco, D., Melzi, S., Gasparini, F., & Jurman, G. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3424 · Cited by 2
Pitfalls in analysis and interpretation of single-cell RNA-seq data in cancer
Tirosh, I. (2025) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdaf047 · Cited by 2
Biology Needs Philosophy, But What Philosophy?
DiFrisco, J., & Orzack, S. H. (2026) · BioScience · doi:10.1093/biosci/biag016 · Cited by 3
scAN1.0: A reproducible and standardized pipeline for processing 10X single cell RNAseq data
Lepetit, M., Ilie, M. D., Chanal, M., Raverot, G., Bertolino, P., Arpin, C., Picard, F., & Gandrillon, O. (2023) · In Silico Biology · doi:10.3233/isb-220252 · Cited by 2
CASCC: a co-expression-assisted single-cell RNA-seq data clustering method
Cai, L., & Anastassiou, D. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae283 · Cited by 2
Preprocessing and Quality Control
Raza, K. (2024) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-981-97-6703-8_2 · Cited by 2
A primer on single‐cell RNA ‐seq analysis using dendritic cells as a case study
Protti, G., & Spreafico, R. (2024) · FEBS Letters · doi:10.1002/1873-3468.15009 · Cited by 2
Collapsible tree: interactive web app to present collapsible hierarchies
Gao, Y., Patro, R., & Jiang, P. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae645 · Cited by 2
Prater, K. E., & Lin, K. (2024) · Glia · doi:10.1002/glia.24633 · Cited by 3
High order expression dependencies finely resolve cryptic states and subtypes in single cell data
Jansma, A., Yao, Y., Wolfe, J. C., Debbio, L. D., Beentjes, S. V., Ponting, C. P., & Khamseh, A. (2025) · Molecular Systems Biology · doi:10.1038/s44320-024-00074-1 · Cited by 2
Chen, A. A., Clark, K. A., Dewey, B. E., DuVal, A. L., Pellegrini, N., Nair, G., Jalkh, Y., Khalil, S., Zurawski, J., Calabresi, P. A., et al. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012241 · Cited by 2
Topological Structure of Population Activity in Mouse Visual Cortex Encodes Visual Scene RotationsPreprint
Beshkov, K., & Einevoll, G. T. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.13.528247 · Cited by 2
Mohseni, N., & Elhaik, E. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.06.570437 · Cited by 2
Mapping the Mind With Free Associations: A Tutorial Using the R Package associatoRPreprint
Aeschbach, S., Mata, R., & Wulff, D. U. (2024) · doi:10.31234/osf.io/ra87s · Cited by 2
A human commons cell atlas reveals cell type specificity for OAS1 isoformsPreprint
Gálvez-Merchán, Á., Booeshaghi, A. S., & Pachter, L. S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.23.586412 · Cited by 4
Self-citationGolden, C. S., Williams, S., Sreerama, S., Blankevoort, S., Yost, H. J., Tristani-Firouzi, M., Belkina, A. C., & Serrano, M. D. L. Á. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.03.616268 · Cited by 2
Collin, A., Pelletier, S., Fierville, M., Droit, A., Precioso, F., Bécavin, C., & Barbry, P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.15.628538 · Cited by 2
Xiao, R., Baptista, R. P., Agyabeng‐Dadzie, F., Li, Y., Dong, Y.-X., Schmitz, R. J., Glenn, T. C., & Kissinger, J. C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.17.633476 · Cited by 2
Birds convey complex signals in simple songs
Snyder, K. T., & Creanza, N. (2024) · Nature · doi:10.1038/d41586-024-00677-6 · Cited by 1
Kwong, K. C. N. K., Philps, B., Smith, F., Sobieska, A., Chen, N., Alabert, C., Bilen, H., & Buonomo, S. B. C. (2025) · Communications Biology · doi:10.1038/s42003-025-07744-2 · Cited by 1
Lipidome visualisation, comparison, and analysis in a vector space
Olzhabaev, T., Müller, L., Krause, D., Schwudke, D., & Torda, A. E. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012892 · Cited by 1
Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data
Hackenberg, M., Guitart, L. C., Backofen, R., & Binder, H. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf287 · Cited by 2
Ding, X., & Ma, R. (2025) · Journal of the American Statistical Association · doi:10.1080/01621459.2025.2539539 · Cited by 4
Marchesi, R., Lazzaro, N., Endrizzi, W., Leonardi, G., Pozzi, M., Ragni, F., Bovo, S., Moroni, M., Osmani, V., & Jurman, G. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013455 · Cited by 1
Smmit: A pipeline for integrating multiple single-cell multi-omics samples
Wan, C., & Ji, Z. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.08.020 · Cited by 1
Considerations in Pursuing Reaction Scope Generality
Zacate, S. B., Dantas, J. A., Lin, S., Doyle, A. G., & Sigman, M. S. (2025) · Angewandte Chemie · doi:10.1002/ange.202511091 · Cited by 1
Improving on early exaggeration in t -SNE: Early hierarchization better preserves global structure
Lee, J. A., Couplet, E., Lambert, P., Merveille, P., Journaux, L., Mulders, D., de Bodt, C., & Verleysen, M. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131882 · Cited by 1
Field Assessment of Force Torque Sensors for Planetary Rover Navigation
Gerdes, L., Pérez-del-Pulgar, C. J., Castilla-Arquillo, R., & Azkarate, M. (2025) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-025-02324-2 · Cited by 1
The cell as a token: high-dimensional geometry in language models and cell embeddings
Gilpin, W. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf595 · Cited by 3
Boosting neural network performance for high dimensional data through random projections
Anagnostou, P., Tasoulis, S. K., Vrahatis, A. G., Georgakopoulos, S. V., & Plagianakos, V. P. (2025) · Pattern Recognition Letters · doi:10.1016/j.patrec.2025.11.006 · Cited by 1
Benchmarking beta-diversity measures and transfer functions for sedimentary ancient DNA
Cordier, T., Keck, F., & Lanzén, A. (2025) · ISME Communications · doi:10.1093/ismeco/ycaf230 · Cited by 1
Visual Analytics using Tensor Unified Linear Comparative Analysis
Okami, N., Miyake, K., Sakamoto, N., Nonaka, J., & Fujiwara, T. (2026) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3633912 · Cited by 2
Hopfield Networks as Models of Emergent Function in Biology
Yampolskaya, M., & Mehta, P. (2026) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-101425-023356 · Cited by 4
NeuroMDAVIS: Visualization of Single-Cell Multi-Omics Data under Deep Learning Framework
Maitra, C., Seal, D. B., Das, V., & De, R. K. (2026) · Journal of Computational Biology · doi:10.1177/15578666261423965 · Cited by 1
Citation distance matters: towards a new metric for evaluating journal impact
Barnes, K., Bannour, A., Török, L., & Molontay, R. (2026) · Scientometrics · doi:10.1007/s11192-026-05586-1 · Cited by 1
Interpretable liquid crystal phase classification via two-by-two ordinal patterns
Voltarelli, L. G. J. M., Osiecka, N., Piwowarczyk, M., Juszyńska‐Gałązka, E., Zola, R. S., Perc, M., & Ribeiro, H. V. (2026) · Physical review. E · doi:10.1103/f6yc-3bcg · Cited by 1
Sankaran, K., Zhang, S., Chenab, & Meilă, M. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag136 · Cited by 1
Dimensionality Reduction Evaluation for Multivariate Quantum Data
Cristani, C. R., & Tessera, D. (2026) · Journal of Data Analysis and Information Processing · doi:10.4236/jdaip.2026.142010 · Cited by 1
ScopeMap: An AI‐Assisted, Human‐in‐the‐Loop Workflow for Mapping Reaction Scope and Boundaries
Li, J., Xiao, X., Yang, Q., Zhao, B., & Luo, S. (2026) · Angewandte Chemie International Edition · doi:10.1002/anie.2455429 · Cited by 1
MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
Huang, Z., Fujiwara, T., Chatzimparmpas, A., Duchemin, W., & Kerren, A. (2026) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2026.3694000 · Cited by 1
Kövér, B., Willis, T. L., Sherwin, O., Kaufman-Cook, J., Kemkem, Y., Segoviano, M. V., Lodge, E. J., Zamojski, M., Mendelev, N., Zhang, Z., et al. (2026) · Yearbook of pediatric endocrinology · doi:10.1016/j.celrep.2026.117407 · Cited by 2
Protein language visualizer: a repository for homology exploration with language model embeddings
Espinoza-Herrera, J., Manríquez-García, M. F., Bermejo, S. M., Jasso, A. L., Ruiz-Alcocer, J. P., Siordia, A., Veskimägi, S. M., Roethler, N., & Jinich, A. (2026) · Digital Discovery · doi:10.1039/d5dd00472a · Cited by 2
A High-Content Imaging Pipeline to Investigate Subcytotoxic Effects in RTgill-W1 Cells
Tome, M., Jozef, B., Mosimann, S. L., Kosnik, M. B., Schirmer, K., & Županič, A. (2026) · Environmental Science & Technology · doi:10.1021/acs.est.5c18316 · Cited by 1
Enriching dimensionality reduction with distortion cues
Hassan, W., Schlieder, A., Sadlo, F., & Nonato, L. G. (2026) · Computers & Graphics · doi:10.1016/j.cag.2026.104724 · Cited by 1
de Groot, D. H., Leonardo, S. X. M., Pachkov, M. V., & van Nimwegen, E. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03220-2 · Cited by 5
Identifying Transient Cells During Reprogramming via Persistent Homology
Petenkaya, A., Manuchehrfar, F., Chronis, C., & Liang, J. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871358 · Cited by 1
q-Diffusion leverages the full dimensionality of gene coexpression in single-cell transcriptomics
Marmarelis, M. G., Littman, R., Battaglin, F., Niedzwiecki, D., Venook, A. P., Ambite, J. L., Galstyan, A., Lenz, H., & Steeg, G. V. (2024) · Communications Biology · doi:10.1038/s42003-024-06104-w · Cited by 1
Graph Vertex Embeddings: Distance, Regularization and Community Detection
Nowak, R., Małkowski, A., Cieślak, D., Sokół, P., & Wawrzyński, P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-63778-0_4 · Cited by 1
Oiva, M., Ohm, T., Mukhina, K. D., Solà, M. C., & Schich, M. (2024) · Journal of Cultural Analytics · doi:10.22148/001c.118495 · Cited by 1
Xu, Y., Zhu, W., Su, Y., Ma, T., Zhang, Y., Pan, X., Huang, R., Li, Y., Zuo, K., Ong, S., et al. (2024) · Vascular Pharmacology · doi:10.1016/j.vph.2024.107417 · Cited by 1
Casey, H. L., Adamou, A., & Rodighiero, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72437-4_8 · Cited by 1
Yang, R., Celino‐Brady, F. T., Dunleavy, J. E. M., Vigh‐Conrad, K. A., Atkins, G. R., Hvasta-Gloria, R., Pombar, C., Yatsenko, A. N., Orwig, K. E., O’Bryan, M. K., et al. (2025) · Biology of Reproduction · doi:10.1093/biolre/ioaf033 · Cited by 2
DiRe - JAX: A JAX based Dimensionality Reduction Algorithm for Large-scale Data
Kolpakov, A., & Rivin, I. (2025) · The Journal of Open Source Software · doi:10.21105/joss.08264 · Cited by 1
GatingTree : Pathfinding Analysis of Group‐Specific Effects in Cytometry Data
Ono, M. (2025) · Cytometry Part A · doi:10.1002/cyto.a.24948 · Cited by 1
Twelve Tips for Reproducible Analysis of Single-Cell Transcriptomics Data
Onfroy, A., Topilko, P., Hüe, S., & Thieffry, D. (2025) · ACM Conference on Reproducibility and Replicability (ACM REP) · doi:10.1145/3736731.3746138 · Cited by 1
Ikonomou, L., Yampolskaya, M., & Mehta, P. (2023) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-26625-6_4 · Cited by 1
Zeng, F., Kong, X., Yang, F., Chen, T., & Han, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.04.531126 · Cited by 1
cellstruct: Metrics scores to quantify the biological preservation between two embeddingsPreprint
Loh, J. W., & Ouyang, J. F. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.13.566337 · Cited by 1
Interpretable models for scRNA-seq data embedding with multi-scale structure preservation
Novák, D., de Bodt, C., Lambert, P., Lee, J. A., Gassen, S. V., & Saeys, Y. (2026) · Cell Systems · doi:10.1101/2023.11.23.568428 · Cited by 4
Characterisation of mesenchymal stromal cells in the skin of Atlantic salmonPreprint
Daniels, R. R., Salisbury, S. J., Sveen, L. R., Taylor, R. S., Vaadal, M., Tengs, T., Monaghan, S. J., Villamayor, P. R., Ballantyne, M., Peñaloza, C., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.23.581759 · Cited by 1
BacSC: A general workflow for bacterial single-cell RNA sequencing data analysisPreprint
Ostner, J., Kirk, T., Olayo-Alarcón, R., Thöming, J. G., Rosenthal, A. Z., Häußler, S., & Müller, C. L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.22.600071 · Cited by 2
TopOMetry systematically learns and evaluates the latent dimensions of single-cell atlasesPreprint
Sidarta-Oliveira, D., Domingos, A. I., & Velloso, L. A. (2024) · eLife · doi:10.7554/elife.100361.1 · Cited by 1
Protein Language Visualizer: A Repository for Homology Exploration with Language Model EmbeddingsPreprint
Espinoza-Herrera, J., Manríquez-García, M. F., Medina-Bermejo, S., López-Jasso, A., Ruiz-Alcocer, J. P., Siordia, A., Veskimägi, S. M., Roethler, N., & Jinich, A. (2026) · bioRxiv · doi:10.1101/2024.11.19.624229 · Cited by 1
Hoerbst, F., Sidhu, G. S., Omori, T., Tomkins, M., & Morris, R. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.20.633909 · Cited by 1
Bica, M., Serre, K., & Barbosa‐Morais, N. L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.17.649161 · Cited by 1
Trimbour, R., Sáez-Rodríguez, J., & Cantini, L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.23.678054 · Cited by 1
Determining gene specificity from multivariate single-cell RNA sequencing dataPreprint
Swarna, N. P., Booeshaghi, A. S., Rebboah, E., Gordon, M. G., Kathail, P., Li, T., Alvarez, M., Ye, C., Wold, B. J., Mortazavi, A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.21.689845 · Cited by 2
Self-citationThe hippocampus as a small-world cognitive mapPreprint
Kim, J. Z., Sethna, J. P., Cohen, I., & Sun, W. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.07.704615 · Cited by 1
Parameter-free representations outperform single-cell foundation models on downstream benchmarksPreprint
Souza, H., & Mehta, P. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.11.705358 · Cited by 1
Fields, C. A., Read, J. F., Coffman, H., Petrow, E. P., Bosco, A., & Bhattacharya, D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.16.706247 · Cited by 1
Fields, C. A., Read, J. F., Coffman, H., Petrow, E. P., Bosco, A., & Bhattacharya, D. (2026) · eLife · doi:10.7554/elife.110953.1 · Cited by 1
Reconstructing disease dynamics for mechanistic insights and clinical benefitPreprint
Frishberg, A., Milman, N., Alpert, A., Spitzer, H., Asani, B., Schiefelbein, J. B., Priglinger, S. G., Schultze, J. L., Theis, F. J., & Shen-Orr, S. S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.17.468952
La Constituciòn tambièn existe: nota de jurisprudencia sobre el trabajo pagado a destajo
Long, A. G. (2007) · Derecho laboral: Revista de doctrina, jurisprudencia e informaciones sociales
Zhang, Y., Gilbert, A. C., & Steinerberger, S. (2022) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton49937.2022.9929350
Computational and Statistical Methods for Single-Cell RNA Sequencing Data
Wang, Z., & Yan, X. (2022) · Springer handbooks of computational statistics · doi:10.1007/978-3-662-65902-1_1
Simple Causal Relationships in Gene Expression Discovered through Deep Learned Collective VariablesPreprint
Wang, C.-H., Vetsigian, K., Lin, C., Firth, F., Bradley, G., Granovsky, L., & England, J. L. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.18.524617
Metadata
- 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
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
