Visualizing structure and transitions in high-dimensional biological data

Untitled work

(2008) · Journal of Machine Learning Research · Cited by 33,051

Nonlinear Dimensionality Reduction by Locally Linear Embedding

Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965

A Global Geometric Framework for Nonlinear Dimensionality Reduction

Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585

Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome

Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., et al. (2009) · Science · doi:10.1126/science.1181369 · Cited by 8,898

Massively parallel digital transcriptional profiling of single cells

Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353

Objective Criteria for the Evaluation of Clustering Methods

Rand, W. M. (1971) · Journal of the American Statistical Association · doi:10.1080/01621459.1971.10482356 · Cited by 6,493

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

Reversed graph embedding resolves complex single-cell trajectories

Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070

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

Diffusion maps

Coifman, R. R., & Lafon, S. (2006) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2006.04.006 · Cited by 2,380

Choroidal endothelial and macrophage gene expression in atrophic and neovascular macular degeneration

Voigt, A. P., Mullin, N. K., Mulfaul, K., Lozano, L. P., Wiley, L. A., Flamme‐Wiese, M. J., Boese, E. A., Han, I. C., Scheetz, T. E., Stone, E. M., et al. (2022) · Human Molecular Genetics · doi:10.1093/hmg/ddac043 · Cited by 64

The molecular architecture of cell cycle arrest

Stallaert, W., Taylor, S. R., Kedziora, K. M., Johnson, M. S., Taylor, C. D., Sobon, H. K., Young, C. L., Limas, J. C., Holloway, J. V., Cook, J. G., et al. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211087 · Cited by 67

Single-cell profiling of alveolar rhabdomyosarcoma reveals RAS pathway inhibitors as cell-fate hijackers with therapeutic relevance

Danielli, S. G., Porpiglia, E., Micheli, A. J. D., Navarro, N., Zellinger, M. J., Bechtold, I., Kisele, S., Volken, L., Ngo, Q. A., Marques, J. G., et al. (2023) · Science Advances · doi:10.1126/sciadv.ade9238 · Cited by 68

Revealing hidden patterns in deep neural network feature space continuum via manifold learning

Islam, M. T., Zhou, Z., Ren, H., Khuzani, M. B., Kapp, D. S., Zou, J., Tian, L., Liao, J. C., & Xing, L. (2023) · Nature Communications · doi:10.1038/s41467-023-43958-w · Cited by 65

Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer

Malla, S. B., Byrne, R. M., Lafarge, M. W., Corry, S. M., Fisher, N. C., Tsantoulis, P. K., Mills, M. L., Ridgway, R. A., Lannagan, T. R. M., Najumudeen, A. K., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01654-5 · Cited by 68

Mass Spectrometry-Based Atlas of Extracellular Matrix Proteins across 25 Mouse Organs

McCabe, M. C., Saviola, A. J., & Hansen, K. C. (2023) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00526 · Cited by 64

Multimodal analyses of vitiligo skin idenitfy tissue characteristics of stable disease

Shiu, J., Zhang, L., Lentsch, G. R., Flesher, J. L., Jin, S., Polleys, C. M., Jo, S. J., Mizzoni, C., Mobasher, P., Kwan, J., et al. (2022) · JCI Insight · doi:10.1172/jci.insight.154585 · Cited by 61

CO and NOx emissions prediction in gas turbine using a novel modeling pipeline based on the combination of deep forest regressor and feature engineering

Coelho, L. D. S., Ayala, H. V. H., & Mariani, V. C. (2023) · Fuel · doi:10.1016/j.fuel.2023.129366 · Cited by 61

PPIL4 is essential for brain angiogenesis and implicated in intracranial aneurysms in humans

Barak, T., Ristori, E., Ercan‐Sencicek, A. G., Miyagishima, D. F., Nelson-Williams, C. J., Dong, W., Jin, S. C., Prendergast, A. E., Armero, W., Henegariu, O., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01572-7 · Cited by 58

MLL-AF9 initiates transformation from fast-proliferating myeloid progenitors

Chen, X., Burkhardt, D. B., Hartman, A. A., Hu, X., Eastman, A. E., Sun, C., Wang, X., Zhong, M., Krishnaswamy, S., & Guo, S. (2019) · Nature Communications · doi:10.1038/s41467-019-13666-5 · Cited by 58

Murine interfollicular epidermal differentiation is gradualistic with GRHL3 controlling progression from stem to transition cell states

Lin, Z., Jin, S., Chen, J. W., Li, Z., Lin, Z., Tang, L., Nie, Q. H., & D, B. A. M. (2020) · Nature Communications · doi:10.1038/s41467-020-19234-6 · Cited by 61

Multiplexed single-cell analysis of organoid signaling networks

Sufi, J., Qin, X., Rodriguez, F. C., Bu, Y. J., Vlckova, P., Zapatero, M. R., Nitz, M., & Tape, C. J. (2021) · Nature Protocols · doi:10.1038/s41596-021-00603-4 · Cited by 54

Immune cells and their inflammatory mediators modify β cells and cause checkpoint inhibitor–induced diabetes

Perdigoto, A. L., Deng, S., Du, K., Kuchroo, M. R., Burkhardt, D. B., Tong, A., Israel, G. M., Robert, M., Weisberg, S. P., Kirkiles-Smith, N. C., et al. (2022) · JCI Insight · doi:10.1172/jci.insight.156330 · Cited by 63

Single-cell analysis of COVID-19, sepsis, and HIV infection reveals hyperinflammatory and immunosuppressive signatures in monocytes

Liu, N., Jiang, C., Cai, P., Shen, Z., Sun, W., Xu, H. Y., Fang, M., Yao, X., Zhu, L., Gao, X., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109793 · Cited by 55

Cranioencephalic functional lymphoid units in glioblastoma

Dobersalske, C., Rauschenbach, L., Hua, Y., Berliner, C., Steinbach, A., Grüneboom, A., Kokkaliaris, K. D., Heiland, D. H., Berger, P., Langer, S., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03152-x · Cited by 57

The landscape of myeloid and astrocyte phenotypes in acute multiple sclerosis lesions

Park, C., Ponath, G. D., Levine-Ritterman, M., Bull, E., Swanson, E. C., Jager, P. L. D., Segal, B. M., & Pitt, D. (2019) · Acta Neuropathologica Communications · doi:10.1186/s40478-019-0779-2 · Cited by 55

DIAPH1 Variants in Non–East Asian Patients With Sporadic Moyamoya Disease

Kundishora, A. J., Peters, S. T., Pinard, A., Durán, D., Panchagnula, S., Barak, T., Miyagishima, D. F., Dong, W., Smith, H. L., Ocken, J., et al. (2021) · JAMA Neurology · doi:10.1001/jamaneurol.2021.1681 · Cited by 55

A combined human gastruloid model of cardiogenesis and neurogenesis

Olmsted, Z. T., & Paluh, J. L. (2022) · iScience · doi:10.1016/j.isci.2022.104486 · Cited by 58

Single-Cell Secretion Analysis Reveals a Dual Role for IL-10 in Restraining and Resolving the TLR4-Induced Inflammatory Response

Alexander, A. F., Kelsey, I., Forbes, H., & Miller‐Jensen, K. (2020) · Cell Reports · doi:10.1016/j.celrep.2021.109728 · Cited by 55

Gene trajectory inference for single-cell data by optimal transport metrics

Qu, R., Cheng, X., Sefik, E., Stanley, J. S., Landa, B., Strino, F., Platt, S., Garritano, J., Odell, I. D., Coifman, R. R., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02186-3 · Cited by 63

Restructuring of plankton genomic biogeography in the surface ocean under climate change

Frémont, P., Gehlen, M., Vrac, M., Leconte, J., Delmont, T. O., Wincker, P., Iudicone, D., & Jaillon, O. (2022) · Nature Climate Change · doi:10.1038/s41558-022-01314-8 · Cited by 57

Cellular dynamics across aged human brains uncover a multicellular cascade leading to Alzheimer’s disease

Green, G. S., Yang, H., Fujita, M., Taga, M., Regev, A., Menon, V., Bennett, D. A. A., Habib, N., & De Jager, P. L. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.083212 · Cited by 51

Model-free control of dynamical systems with deep reservoir computing

Canaday, D., Pomerance, A., & Gauthier, D. J. (2021) · Journal of Physics Complexity · doi:10.1088/2632-072x/ac24f3 · Cited by 50

Transcriptional space-time mapping identifies concerted immune and stromal cell patterns and gene programs in wound healing and cancer

Hu, K. H., Kuhn, N. F., Courau, T., Tsui, J. L., Samad, B., Ha, P. K., Kratz, J. R., Combes, A. J., & Krummel, M. F. (2023) · Cell stem cell · doi:10.1016/j.stem.2023.05.001 · Cited by 52

Global branches and local states of the human gut microbiome define associations with environmental and intrinsic factors

Tap, J., Lejzerowicz, F., Cotillard, A., Pichaud, M., McDonald, D. T., Song, S. J., Knight, R. D., Veiga, P., & Derrien, M. (2023) · Nature Communications · doi:10.1038/s41467-023-38558-7 · Cited by 57

The 3D Genome Structure of Single Cells

Zhou, T., Zhang, R., & Ma, J. (2021) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020121-084709 · Cited by 51

Generation of specialized blood vessels via lymphatic transdifferentiation

Das, R. N., Tevet, Y., Safriel, S., Han, Y., Moshe, N., Lambiase, G., Bassi, I., Nicenboim, J., Brückner, M., Hirsch, D., et al. (2022) · Nature · doi:10.1038/s41586-022-04766-2 · Cited by 50

Tissue-embedded stretchable nanoelectronics reveal endothelial cell–mediated electrical maturation of human 3D cardiac microtissues

Lin, Z., Garbern, J. C., Liu, R., Li, Q., Juncosa, E. M., Elwell, H., Sokol, M. K., Aoyama, J., Deumer, U.-S., Hsiao, E., et al. (2023) · Science Advances · doi:10.1126/sciadv.ade8513 · Cited by 51

Developmental origin of oligodendrocytes determines their function in the adult brain

Foerster, S., Floriddia, E. M., van Bruggen, D., Kukanja, P., Hervé, B., Cheng, S., Kim, E., Phillips, B. U., Heath, C. J., Tripathi, R. B., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01666-8 · Cited by 53

How to Get Started with Single Cell RNA Sequencing Data Analysis

Balzer, M. S., Ma, Z., Zhou, J., Abedini, A., & Suszták, K. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2020121742 · Cited by 51

MENDER: fast and scalable tissue structure identification in spatial omics data

Yuan, Z. (2024) · Nature Communications · doi:10.1038/s41467-023-44367-9 · Cited by 52

Protective role for kidney TREM2high macrophages in obesity- and diabetes-induced kidney injury

Subramanian, A., Vernon, K. A., Zhou, Y., Marshall, J. L., Alimova, M., Arevalo, C., Zhang, F., Slyper, M., Waldman, J., Montesinos, M. S., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114253 · Cited by 47

Gliomagenesis mimics an injury response orchestrated by neural crest-like cells

Hamed, A. A., Hua, K., Trinh, Q. M., Simons, B. D., Marioni, J. C., Stein, L. D., & Dirks, P. B. (2025) · Nature · doi:10.1038/s41586-024-08356-2 · Cited by 47

The spatial landscape of glial pathology and T cell response in Parkinson’s disease substantia nigra

Ma, M., Paryani, F., Jakubiak, K. A., Xia, S., Antoku, S., Kannan, A., Lee, J., Madden, N., Kumar, S. S., Li, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62478-3 · Cited by 61

Uncovering axes of variation among single-cell cancer specimens

Chen, W. S., Zivanovic, N., van Dijk, D., Wolf, G., Bodenmiller, B., & Krishnaswamy, S. (2020) · Nature Methods · doi:10.1038/s41592-019-0689-z · Cited by 48

A machine-learning-based alloy design platform that enables both forward and inverse predictions for thermo-mechanically controlled processed (TMCP) steel alloys

Lee, J.-W., Park, C., Lee, B. D., Park, J., Goo, N. H., & Sohn, K. (2021) · Scientific Reports · doi:10.1038/s41598-021-90237-z · Cited by 50

Artificial Intelligence and Machine Learning: What You Always Wanted to Know but Were Afraid to Ask

Rattan, P., Penrice, D. D., & Simonetto, D. A. (2022) · Gastro Hep Advances · doi:10.1016/j.gastha.2021.11.001 · Cited by 59

Advances in the Application of Single-Cell Transcriptomics in Plant Systems and Synthetic Biology

Islam, M. T., Liu, Y., Hassan, M. M., Abraham, P. E., Merlet, J., Townsend, A., Jacobson, D., Buell, C. R., Tuskan, G. A., & Yang, X. (2024) · BioDesign Research · doi:10.34133/bdr.0029 · Cited by 51

AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential

Ma, C., Zhang, H., Rao, Y., Jiang, X., Liu, B., Sun, Z., Song, Z., Gao, Y., Cui, Y., Liu, X., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02198-6 · Cited by 61

Single-Cell Expression Variability Implies Cell Function

Osorio, D., Yu, X., Zhong, Y., Li, G., Yu, P., Serpedin, E., Huang, J., & Cai, J. J. (2019) · Cells · doi:10.3390/cells9010014 · Cited by 45

Single-cell analysis of prostaglandin E2-induced human decidual cell in vitro differentiation: a minimal ancestral deciduogenic signal

Stadtmauer, D. J., & Wagner, G. P. (2021) · Biology of Reproduction · doi:10.1093/biolre/ioab183 · Cited by 45

Single Cell RNA-seq and Mass Cytometry Reveals a Novel and a Targetable Population of Macrophages in Idiopathic Pulmonary FibrosisPreprint

Ayaub, E., Poli, S., Ng, J., Adams, T. S., Schupp, J. C., Quesada-Arias, L. D., Poli, F., Cosme, C., Robertson, M. J., Manzano, J. M. M., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.04.425268 · Cited by 48

Multi-view manifold learning of human brain-state trajectories

Busch, E. L., Huang, J., Benz, A., Wallenstein, T., Lajoie, G., Wolf, G., Krishnaswamy, S., & Turk‐Browne, N. B. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00419-0 · Cited by 41

Comparative analysis of dimension reduction methods for cytometry by time-of-flight data

Wang, K., Yang, Y., Wu, F., Song, B., Wang, X., & Wang, T. (2023) · Nature Communications · doi:10.1038/s41467-023-37478-w · Cited by 47

The N-glycome regulates the endothelial-to-hematopoietic transition

Kasper, D. M., Hintzen, J., Wu, Y., Ghersi, J. J., Mandl, H. K., Salinas, K. E., Armero, W., He, Z., Sheng, Y., Xie, Y., et al. (2020) · Science · doi:10.1126/science.aaz2121 · Cited by 45

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 44

Information geometry for multiparameter models: new perspectives on the origin of simplicity

Quinn, K. N., Abbott, M. C., Transtrum, M. K., Machta, B. B., & Sethna, J. P. (2022) · Reports on Progress in Physics · doi:10.1088/1361-6633/aca6f8 · Cited by 62

Imputing single-cell RNA-seq data by considering cell heterogeneity and prior expression of dropouts

Zhang, L., & Zhang, S. (2020) · Journal of Molecular Cell Biology · doi:10.1093/jmcb/mjaa052 · Cited by 45

Human and murine fibroblast single-cell transcriptomics reveals fibroblast clusters are differentially affected by ageing and serum cholesterol

van Kuijk, K., McCracken, I. R., Tillie, R. J. H. A., Asselberghs, S., Kheder, D. A., Muitjens, S., Jin, H., Taylor, R. S., Schreur, R. W., Kuppe, C., et al. (2023) · Cardiovascular Research · doi:10.1093/cvr/cvad016 · Cited by 44

Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges

Nie, X., Qin, D., Zhou, X., Duo, H., Hao, Y., Li, B., & Liang, G. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106939 · Cited by 44

Oral Dysbiosis and Neurodegenerative Diseases: Correlations and Potential Causations

Nicholson, J. S., & Landry, K. S. (2022) · Microorganisms · doi:10.3390/microorganisms10071326 · Cited by 43

Analysis of the microglia transcriptome across the human lifespan using single cell RNA sequencing

Yaqubi, M., Groh, A. M. R., Dorion, M., Afanasiev, E., Luo, J. X. X., Hashemi, H., Sinha, S., Kieran, N. W., Blain, M., Cui, Q., et al. (2023) · Journal of Neuroinflammation · doi:10.1186/s12974-023-02809-7 · Cited by 45

Single-cell transcriptome profiling reveals immune and stromal cell heterogeneity in primary Sjögren’s syndrome

Xiang, N., Xu, H., Zhou, Z., Wang, J., Cai, P., Wang, L., Tan, Z., Zhou, Y., Zhang, T., Zhou, J., et al. (2023) · iScience · doi:10.1016/j.isci.2023.107943 · Cited by 45

Reconstruction of Ewing Sarcoma Developmental Context from Mass-Scale Transcriptomics Reveals Characteristics of EWSR1-FLI1 Permissibility

Miller, H. E., Gorthi, A., Bassani, N., Lawrence, L. A., Iskra, B. S., & Bishop, A. J. R. (2020) · Cancers · doi:10.3390/cancers12040948 · Cited by 42

Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence

Rieck, B., Yates, T. S., Bock, C., Borgwardt, K. M., Wolf, G., Turk‐Browne, N. B., & Krishnaswamy, S. (2020) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000462843 · Cited by 40

SOTIP is a versatile method for microenvironment modeling with spatial omics data

Yuan, Z., Li, Y., Shi, M., Yang, F., Gao, J. T., Yao, J., & Zhang, M. Q. (2022) · Nature Communications · doi:10.1038/s41467-022-34867-5 · Cited by 40

Cardiomyocyte ploidy is dynamic during postnatal development and varies across genetic backgrounds

Swift, S. K., Purdy, A. L., Kolell, M. E., Flinn, M. A., Lahue, C., Buddell, T., Akins, K. A., Foster, P., O'Meara, C. C., Rau, C. D., et al. (2023) · Development · doi:10.1242/dev.201318 · Cited by 40

A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold

Fei, Y., Liu, Y., Jia, C., Zheng-yu, L., Wei, X. H., & Chen, M. (2024) · ACM Computing Surveys · doi:10.1145/3708498 · Cited by 50

A genome-wide optical pooled screen reveals regulators of cellular antiviral responses

Carlson, R. J., Leiken, M. D., Guna, A., Hacohen, N., & Blainey, P. C. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2210623120 · Cited by 33

CD4+ T cells exhibit distinct transcriptional phenotypes in the lymph nodes and blood following mRNA vaccination in humans

Borcherding, N. C., Kim, W., Quinn, M. A., Han, F., Zhou, J. Q., Sturtz, A. J., Schmitz, A. J., Lei, T., Schattgen, S., Klebert, M. K., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01888-9 · Cited by 36

Critical role for a high-plasticity cell state in lung cancer

Chan, J. E., Pan, C., Rub, J., Guzman, G., Bastl, K., Brown, E., Zhang, Z., Styers, H. C., Hartmann, G. G., Li, Z., et al. (2026) · Nature · doi:10.1038/s41586-025-09985-x · Cited by 38

Longitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1

Tejwani, L., Ravindra, N. G., Lee, C., Cheng, Y., Nguyen, B., Luttik, K. P., Ni, L., Zhang, S., Morrison, L. M., Gionco, J. T., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.10.039 · Cited by 37

Sentimental Analysis of Twitter Comments on Covid-19

Raheja, S., & Asthana, A. K. (2021) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence51648.2021.9377048 · Cited by 37

Metabolic collateral lethal target identification reveals MTHFD2 paralogue dependency in ovarian cancer

Achreja, A., Yu, T., Mittal, A., Choppara, S., Animasahun, O. H., Nenwani, M., Wuchu, F., Meurs, N., Mohan, A., Jeon, J. H., et al. (2022) · Nature Metabolism · doi:10.1038/s42255-022-00636-3 · Cited by 33

Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry

Doan, M., Barnes, C. M., McQuin, C., Caicedo, J. C., Goodman, A., Carpenter, A. E., & Rees, P. C. T. (2021) · Nature Protocols · doi:10.1038/s41596-021-00549-7 · Cited by 35

Metatranscriptomics captures dynamic shifts in mycorrhizal coordination in boreal forests

Law, S. R., Serrano, A. R., Daguerre, Y., Sundh, J., Schneider, A. N., Stangl, Z. R., Castro, D., Grabherr, M. G., Näsholm, T., Street, N. R., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2118852119 · Cited by 35

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Joodaki, M., Shaigan, M., Parra, V., Bülow, R. D., Kuppe, C., Hölscher, D. L., Cheng, M., Nagai, J. S., Goedertier, M., Bouteldja, N., et al. (2023) · Molecular Systems Biology · doi:10.1038/s44320-023-00003-8 · Cited by 40

SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains

Rahimikollu, J., Xiao, H., Rosengart, A., Rosen, A. B. I., Tabib, T., Zdinak, P. M., He, K., Bing, X., Bunea, F., Wegkamp, M. H., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02175-z · Cited by 35

Gene networks and systems biology in Alzheimer's disease: Insights from multi‐omics approaches

Rahimzadeh, N., Srinivasan, S. S., Zhang, J., & Swarup, V. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.13790 · Cited by 39

A data-driven dimensionality-reduction algorithm for the exploration of patterns in biomedical data

Islam, M. T., & Xing, L. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00635-3 · Cited by 33

Characterization of naked mole‐rat hematopoiesis reveals unique stem and progenitor cell patterns and neotenic traits

Emmrich, S., Trapp, A., Zakusilo, F. T., Straight, M. E., Ying, K., Tyshkovskiy, A., Mariotti, M., Gray, S., Zhang, Z., Drage, M. G., et al. (2022) · The EMBO Journal · doi:10.15252/embj.2021109694 · Cited by 32

Functional analysis of cell plasticity using single-cell technologies

Qin, X., & Tape, C. J. (2024) · Trends in Cell Biology · doi:10.1016/j.tcb.2024.01.006 · Cited by 32

Advancing optical nanosensors with artificial intelligence: A powerful tool to identify disease-specific biomarkers in multi-omics profiling

Taha, B. A., Abdulrahm, Z. M., Addie, A. J., Haider, A. J., Alkawaz, A. N., Yaqoob, I. A. M., & Arsad, N. (2025) · Talanta · doi:10.1016/j.talanta.2025.127693 · Cited by 34

PsychENCODE and beyond: transcriptomics and epigenomics of brain development and organoids

Jourdon, A., Scuderi, S., Capauto, D., Abyzov, A. S., & Vaccarino, F. M. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0763-3 · Cited by 29

Morphological pseudotime ordering and fate mapping reveal diversification of cerebellar inhibitory interneurons

Wang, W. X., & Lefebvre, J. L. (2022) · Nature Communications · doi:10.1038/s41467-022-30977-2 · Cited by 35

Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET

Pandey, K., & Zafar, H. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac412 · Cited by 34

Groundwater fauna in an urban area – natural or affected?

Koch, F., Menberg, K., Schweikert, S., Spengler, C., Hahn, H. J., & Blum, P. (2021) · Hydrology and earth system sciences · doi:10.5194/hess-25-3053-2021 · Cited by 31

Live imaging reveals chromatin compaction transitions and dynamic transcriptional bursting during stem cell differentiation in vivo

May, D. P., Yun, S., Gonzalez, D. G., Park, S., Chen, Y., Lathrop, E., Cai, B., Xin, T., Zhao, H., Wang, S., et al. (2023) · eLife · doi:10.7554/elife.83444 · Cited by 34

Mapping the cellular landscape of Atlantic salmon head kidney by single cell and single nucleus transcriptomics

Andresen, A. M. S., Taylor, R. S., Grimholt, U., Daniels, R. R., Sun, J., Dobie, R., Henderson, N. C., Martín, S., Macqueen, D. J., & Fosse, J. H. (2024) · Fish & Shellfish Immunology · doi:10.1016/j.fsi.2024.109357 · Cited by 32

The promise of single-cell genomics in plants

McFaline‐Figueroa, J. L., Trapnell, C., & Cuperus, J. T. (2020) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2020.04.002 · Cited by 31

NR2E3 loss disrupts photoreceptor cell maturation and fate in human organoid models of retinal development

Mullin, N. K., Bohrer, L. R., Voigt, A. P., Lozano, L. P., Wright, A. T., Bonilha, V. L., Mullins, R. F., Stone, E. M., & Tucker, B. A. (2024) · Journal of Clinical Investigation · doi:10.1172/jci173892 · Cited by 29

Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data

Stock, M., Losert, C., Zambon, M., Popp, N., Lubatti, G., Hörmanseder, E., Heinig, M., & Scialdone, A. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00088-3 · Cited by 34

Preventing Cholesterol-Induced Perk (Protein Kinase RNA-Like Endoplasmic Reticulum Kinase) Signaling in Smooth Muscle Cells Blocks Atherosclerotic Plaque Formation

Chattopadhyay, A., Guan, P., Majumder, S., Kaw, K., Zhou, Z., Zhang, C., Prakash, S. K., Kaw, A., Buja, L. M., Kwartler, C. S., et al. (2022) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.121.317451 · Cited by 29

Interpretable Machine Learning Assessment

Han, H., Wu, Y., Wang, J., & Han, A. (2022) · Neurocomputing · doi:10.1016/j.neucom.2023.126891 · Cited by 31

A transcriptome-based human universal senescence index (hUSI) robustly predicts cellular senescence under various conditions

Wang, J., Zhou, X., Yu, P., Yao, J., Guo, P., Xu, Q., Zhao, Y., Wang, G., Li, Q., Zhu, X., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00886-2 · Cited by 28

Cell cycle controls long-range calcium signaling in the regenerating epidermis

Moore, J. L., Bhaskar, D., Gao, F., Matte-Martone, C. C., Du, S., Lathrop, E., Ganesan, S., Shao, L., Norris, R. P., Sanz, N. C., et al. (2023) · The Journal of Cell Biology · doi:10.1083/jcb.202302095 · Cited by 28

Deciphering Organoids: High-Dimensional Analysis of Biomimetic Cultures

Qin, X., & Tape, C. J. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.10.013 · Cited by 27

Single-cell RNA sequencing reveals cellular and molecular heterogeneity in fibrocartilaginous enthesis formation

Zhang, T., Wan, L., Xiao, H., Wang, L., Hu, J., & Lü, H. (2023) · eLife · doi:10.7554/elife.85873 · Cited by 28

Dynamic molecular atlas of cardiac fibrosis at single-cell resolution shows CD248 in cardiac fibroblasts orchestrates interactions with immune cells

Li, G., Ni, C., Wang, J., Zhang, F., Fu, Z., Wang, L., Wang, B., Liu, Y., Zhao, J., Li, M., et al. (2025) · Nature Cardiovascular Research · doi:10.1038/s44161-025-00617-1 · Cited by 27

Visualizing Single-Cell RNA-seq Data with Semisupervised Principal Component Analysis

Liu, Z. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21165797 · Cited by 27

Astrocyte Heterogeneity in Multiple Sclerosis: Current Understanding and Technical Challenges

Lo, C. H., Škarica, M., Mansoor, M., Bhandarkar, S., Toro, S., & Pitt, D. (2021) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2021.726479 · Cited by 27

CellexalVR: A virtual reality platform to visualize and analyze single-cell omics data

Legetth, O., Rodhe, J., Lang, S., Dhapola, P., Wallergård, M., & Soneji, S. (2021) · iScience · doi:10.1016/j.isci.2021.103251 · Cited by 31

A comparison of rule-based and centroid single-sample multiclass predictors for transcriptomic classification

Eriksson, P., Marzouka, N., Sjödahl, G., Bernardo, C., Liedberg, F., & Höglund, M. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab763 · Cited by 27

Regional and age‐related diversity of human mature oligodendrocytes

Yaqubi, M., Luo, J. X. X., Baig, S., Cui, Q., Petrecca, K., Desu, H. L., Larochelle, C., Afanasiev, E., Hall, J. A., Dudley, R., et al. (2022) · Glia · doi:10.1002/glia.24230 · Cited by 28

A Bayesian inference transcription factor activity model for the analysis of single-cell transcriptomesPreprint

Gao, S., Dai, Y. D., & Rehman, J. (2021) · Genome Research · doi:10.1101/gr.265595.120 · Cited by 24

Cell cycle plasticity underlies fractional resistance to palbociclib in ER+/HER2− breast tumor cells

Zikry, T. M., Wolff, S. C., Ranek, J. S., Davis, H. M., Naugle, A., Luthra, N., Whitman, A. A., Kedziora, K. M., Stallaert, W., Kosorok, M. R., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2309261121 · Cited by 31

An overview of computational methods in single-cell transcriptomic cell type annotation

Li, T., Wang, Z., Liu, Y., He, S., Zou, Q., & Zhang, Y. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf207 · Cited by 28

EpiAgent: foundation model for single-cell epigenomics

Chen, X., Li, K., Cui, X., Wang, Z., Jiang, Q., Lin, J., Li, Z., Gao, Z., Lv, H., & Jiang, R. (2025) · Nature Methods · doi:10.1038/s41592-025-02822-z · Cited by 32

1-deoxysphingolipids bind to COUP-TF to modulate lymphatic and cardiac cell development

Wang, T., Wang, Z., de Fabritus, L., Tao, J., Saied, E. M., Lee, H., Ramazanov, B. R., Jackson, B., Burkhardt, D. B., Parker, M., et al. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.10.018 · Cited by 23

Prdm6 controls heart development by regulating neural crest cell differentiation and migration

Hong, L., Li, N., Gasque, V., Mehta, S. S., Ye, L., Wu, Y., Li, J., Gewies, A., Ruland, J., Hirschi, K. K., et al. (2022) · JCI Insight · doi:10.1172/jci.insight.156046 · Cited by 27

Visualizing Structure and Transitions for Biological Data ExplorationPreprint

Moon, K. R., van Dijk, D., Wang, Z., Gigante, S., Burkhardt, D. B., Chen, W. S., Yim, K., van den Elzen, A. M. G., Hirn, M. J., Coifman, R. R., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/120378 · Cited by 56

High-dimensional data visualization

Tang, L. (2020) · Nature Methods · doi:10.1038/s41592-020-0750-y · Cited by 5

Hyperbolic PHATE: Visualizing Continuous Hierarchy of Latent Differentiation Structures

Nakano, M., Sakuma, H., Nishikimi, R., Komiya, K., Iwata, T., & Kashino, K. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890074

Exploring a world of a thousand dimensions

Vallejos, C. A. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0330-9 · Cited by 16

Dimensionality reduction for visualizing high-dimensional biological data

Malepathirana, T., Senanayake, D., Vidanaarachchi, R., Gautam, V., & Halgamuge, S. K. (2022) · Biosystems · doi:10.1016/j.biosystems.2022.104749 · Cited by 15