Visualizing structure and transitions in high-dimensional biological data

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

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

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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

Cancer-myeloid cell invasive program in pediatric-type diffuse high-grade gliomaPreprint

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Robust and efficient annotation of cell states through gene signature scoringPreprint

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Scalable multimodal mapping of macrophage regulatory architecture by integrating optical and transcriptomic pooled screensPreprint

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A multimodal perturbation atlas defines the phenotypic resolution of cellular morphologyPreprint

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Revealing trajectories of the mind via non-linear manifolds of brain activity

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Dissimilarity analysis based on diffusion maps

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K-Hyperparameter Tuning in High-Dimensional Space Clustering: Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes

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Interpretable models for scRNA-seq data embedding with multi-scale structure preservation

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High cell throughput, programmable fixation reveals the RNA and protein co-regulation with spatially resolved NFκB pseudo-signaling

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Evolutionary innovations in germline biology of placental mammals identified by transcriptomics of first-wave spermatogenesis in opossum

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ADM: adaptive graph diffusion for meta-dimension reduction

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Inferring single-cell trajectories via critical cell identification using graph centrality algorithm

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Esr1-dependent signaling and transcriptional maturation in the medial preoptic area of the hypothalamus shape the development of mating behavior during adolescence

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Single-cell analysis of pig lung leukocytes and their response to influenza infection and oseltamivir therapy

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Influence of melanin and macrophage activation on hearing loss in SLC26A4 deficient mice.

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Cluster-independent multiscale marker identification in single-cell RNA-seq data using localized marker detector (LMD)

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Chronic alcohol consumption induces phenotypic and functional alterations consistent with a hyper-inflammatory state in peripheral blood mononuclear cell-derived microglia in a rhesus macaque model

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Reconstructing epigenomic dynamics through a single-cell multi-epigenome data integration framework

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Size‐Modulated Mesoderm‐Endoderm Divergence and Myocardial Cavitation in Micropatterned Cardioids

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Deep learning-based physiological risk stratification in night-shift hospital workers

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ZFP36L2 orchestrates stress-adaptive plasticity in regeneration and cancer

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Bonsai reconstructs tree representations for distortion-free visualization and exploration of high-dimensional data

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Towards a Quantitative Understanding of Cellular Dynamics via Lineage Tracing Inference

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CardamomOT: A mechanistic optimal transport-based framework for gene regulatory network inference, trajectory reconstruction and generative modeling

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Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering

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A Geometry-Driven Longitudinal Topic Model

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Towards Network Medicine: Implementation of Panomics and Artificial Intelligence for Precision Medicine

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RobustTree: An adaptive, robust PCA algorithm for embedded tree structure recovery from single-cell sequencing data

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SpectralMAP: Approximating Data Manifold With Spectral Decomposition

Watanabe, K., Maeda, K., Ogawa, T., & Haseyama, M. (2023) · IEEE Access · doi:10.1109/access.2023.3257427 · Cited by 2

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A Flow Artist for High-Dimensional Cellular Data

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Graph Fourier MMD for Signals on Graphs

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q-Diffusion leverages the full dimensionality of gene coexpression in single-cell transcriptomics

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Temporal shift in groundwater fauna in southwestern Germany

Koch, F., Blum, P., Stein, H., Fuchs, A., Hahn, H. J., & Menberg, K. (2024) · Hydrology and earth system sciences · doi:10.5194/hess-28-4927-2024 · Cited by 1

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Fuzzy-Based Identification of Transition Cells to Infer Cell Trajectory for Single-Cell Transcriptomics

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Sroczynski, D. W., Kemeth, F. P., Georgiou, A., Coifman, R. R., & Kevrekidis, I. G. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf018 · Cited by 1

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Unified Visual-Aware Representations for Data Analytics

Peška, L., Sixtová, I., Hoksza, D., Bernhauer, D., Lokoč, J., & Skopal, T. (2025) · IEEE Access · doi:10.1109/access.2025.3534330 · Cited by 3

SurvSig: Harnessing gene expression signatures to uncover heterogeneity in lung neuroendocrine neoplasms

Nemes, K., Fűr, G. M., Benő, A., Schultz, C. W., Topolcsányi, P., Magó, É., Desai, P. A., Takahashi, N., Aladjem, M. I., Reinhold, W. C., et al. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.06.010 · Cited by 1

Bayesian Functional Data Analysis in Astronomy

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Gómez-Talal, I., Bote-Curiel, L., Lozano-Paredes, D., Feijoo-Martínez, J. R., & Rojo‐Álvarez, J. L. (2026) · IEEE Access · doi:10.1109/access.2026.3662155 · Cited by 2

Geometric quantification of cell phenotype transition manifolds with information geometry

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Zang, Z., Wang, Y., Wu, J., Liu, H., Shen, Y., Lei, Z., & Li, S. Z. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3705888 · Cited by 1

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Kuchroo, M. R., Godavarthi, A., Wolf, G., & Krishnaswamy, S. (2021) · arXiv (Cornell University) · Cited by 1

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Ryabov, A. B., Blasius, B., Hillebrand, H., Olenina, I., & Groß, T. (2021) · arXiv (Cornell University) · Cited by 1

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Secrier, M., Wiecek, A. J., Cutty, S. J., Kornai, D., Parreno-Centeno, M., Gourmet, L. E., Tagliazucchi, G. M., Jacobson, D. H., Zhang, P., Xiong, L., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-1104031/v1 · Cited by 2

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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. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.03.470971 · Cited by 1

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Yuan, Z., Li, Y., Shi, M., Yang, F., Gao, J. T., Yao, J., & Zhang, M. Q. (2022) · Research Square · doi:10.21203/rs.3.rs-1263025/v1 · Cited by 1

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Cherry, C., Andorko, J. I., Krishnan, K., Mejías, J. C., Nguyen, H. H., Stivers, K. B., Gray-Gaillard, E. F., Ruta, A., Hamada, N., Hamada, M., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.22.485297 · Cited by 1

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Anaissi, A., Zandavi, S. M., Suleiman, B., Alyassine, W., Braytee, A., & Vafaee, F. (2022) · Research Square · doi:10.21203/rs.3.rs-2249309/v1 · Cited by 2

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Gu, J., Guo, W., Li, X., Wang, D., Yan, N., Hu, Q., Yang, F., Zhang, X., & Yao, J. (2023) · Research Square · doi:10.21203/rs.3.rs-3749896/v1 · Cited by 1

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Winn-Nuñez, E. T., Witt, H. A., Bhaskar, D., Huang, R. Y., Reichner, J. S., Wong, I. Y., & Crawford, L. A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.09.574919 · Cited by 1

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