UMAP: Uniform Manifold Approximation and Projection

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

McInnes,%20Leland%20and%20Healy,%20John%20and%20Saul,%20Nathaniel%20and%20Grossberger,%20Lukas, UMAP, 2018, https://github.com/lmcinnes/umap, 2018-07-22

(n.d.)

Fast, sensitive and accurate integration of single-cell data with Harmony

Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488

The mutational constraint spectrum quantified from variation in 141,456 humans

Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B., Alföldi, J., Wang, Q., Collins, R. L., Laricchia, K. M., Ganna, A., Birnbaum, D. P., et al. (2020) · Nature · doi:10.1038/s41586-020-2308-7 · Cited by 9,434

Inference and analysis of cell-cell communication using CellChat

Jin, S., Guerrero-Juarez, C. F., Zhang, L., Chang, I., Ramos, R., Kuan, C.-H., Myung, P., Plikus, M. V., & Nie, Q. (2021) · Nature Communications · doi:10.1038/s41467-021-21246-9 · Cited by 8,409

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

Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

Hafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479

The single-cell transcriptional landscape of mammalian organogenesis

Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490

Scrublet: computational identification of cell doublets in single-cell transcriptomic data

Wolock, S. L., Lopez, R., & Klein, A. M. (2019) · Cell Systems · doi:10.1016/j.cels.2018.11.005 · Cited by 2,758

Generalizing RNA velocity to transient cell states through dynamical modeling

Bergen, V., Lange, M., Peidli, S., Wolf, F. A., & Theis, F. J. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0591-3 · Cited by 2,951

Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH

Eng, C.-H. L., Lawson, M., Zhu, Q., Dries, R., Koulena, N., Takei, Y., Yun, J., Cronin, C., Karp, C., Yuan, G.-C., et al. (2019) · Nature · doi:10.1038/s41586-019-1049-y · Cited by 1,754

The mutational constraint spectrum quantified from variation in 141,456 humans

Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B., Alföldi, J., Wang, Q., Collins, R. L., Laricchia, K. M., Ganna, A., Birnbaum, D. P., et al. (2019) · doi:10.1101/531210 · Cited by 1,662

Single-cell chromatin state analysis with Signac

Stuart, T., Srivastava, A., Madad, S., Lareau, C. A., & Satija, R. (2021) · Nature Methods · doi:10.1038/s41592-021-01282-5 · Cited by 1,769

Text Data Augmentation for Deep Learning

Shorten, C., Khoshgoftaar, T. M., & Furht, B. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00492-0 · Cited by 1,852

Cells of the adult human heart

Litviňuková, M., Talavera-López, C., Maatz, H., Reichart, D., Worth, C. L., Lindberg, E. L., Kanda, M., Polanski, K., Heinig, M., Lee, M., et al. (2020) · Nature · doi:10.1038/s41586-020-2797-4 · Cited by 1,729

PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells

Wolf, F. A., Hamey, F. K., Plass, M., Solana, J., Dahlin, J. S., Göttgens, B., Rajewsky, N., Simon, L., & Theis, F. J. (2019) · Genome Biology · doi:10.1186/s13059-019-1663-x · Cited by 1,698

Clonal replacement of tumor-specific T cells following PD-1 blockade

Yost, K. E., Satpathy, A. T., Wells, D. K., Qi, Y., Wang, C., Kageyama, R., McNamara, K. L., Granja, J. M., Sarin, K. Y., Brown, R. A., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0522-3 · Cited by 1,534

Single-cell transcriptional diversity is a hallmark of developmental potential

Gulati, G. S., Sikandar, S. S., Wesche, D. J., Manjunath, A., Bharadwaj, A., Berger, M. J., Ilagan, F., Kuo, A. H., Hsieh, R. W., Cai, S., et al. (2020) · Science · doi:10.1126/science.aax0249 · Cited by 1,571

ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis

Granja, J. M., Corces, M. R., Pierce, S. E., Bagdatli, S. T., Choudhry, H., Chang, H. Y., & Greenleaf, W. J. (2021) · Nature Genetics · doi:10.1038/s41588-021-00790-6 · Cited by 1,392

Benchmarking atlas-level data integration in single-cell genomics

Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01336-8 · Cited by 1,438

Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

Maynard, K. R., Collado-Torres, L., Weber, L. M., Uytingco, C., Barry, B. K., Williams, S. R., Catallini, J. L., Tran, M. N., Besich, Z., Tippani, M., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00787-0 · Cited by 1,222

Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin

Ma, S., Zhang, B., LaFave, L. M., Earl, A. S., Chiang, Z., Hu, Y., Ding, J., Brack, A., Kartha, V. K., Tay, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.056 · Cited by 1,146

A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation

Yao, Z., van Velthoven, C. T. J., Nguyen, T. N., Goldy, J., Sedeno-Cortes, A. E., Baftizadeh, F., Bertagnolli, D., Casper, T., Chiang, M., Crichton, K., et al. (2021) · Cell · doi:10.1016/j.cell.2021.04.021 · Cited by 1,065

Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma

Ji, A. L., Rubin, A. J., Thrane, K., Jiang, S., Reynolds, D. L., Meyers, R. M., Guo, M. G., George, B. M., Mollbrink, A., Bergenstråhle, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.039 · Cited by 1,117

A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain

Yao, Z., van Velthoven, C. T. J., Kunst, M., Zhang, M., McMillen, D., Lee, C., Jung, W., Goldy, J., Abdelhak, A., Aitken, M., et al. (2023) · Nature · doi:10.1038/s41586-023-06812-z · Cited by 1,203

Challenges in unsupervised clustering of single-cell RNA-seq data

Kiselev, V. Y., Andrews, T. S., & Hemberg, M. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-018-0088-9 · Cited by 1,205

Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets

(2019) · doi:10.5194/hess-23-5089-2019 · Cited by 1,223

An atlas of the protein-coding genes in the human, pig, and mouse brain

Sjöstedt, E., Zhong, W., Fagerberg, L., Karlsson, M., Mitsios, N., Adori, C., Oksvold, P., Edfors, F., Limiszewska, A., Hikmet, F., et al. (2020) · Science · doi:10.1126/science.aay5947 · Cited by 1,151

The art of using t-SNE for single-cell transcriptomics

Kobak, D., & Berens, P. (2019) · Nature Communications · doi:10.1038/s41467-019-13056-x · Cited by 1,161

Disease-associated astrocytes in Alzheimer’s disease and aging

Habib, N., McCabe, C., Medina, S., Varshavsky, M., Kitsberg, D., Dvir-Szternfeld, R., Green, G., Dionne, D., Nguyen, L., Marshall, J. L., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0624-8 · Cited by 1,009

TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment

Sun, D., Wang, J., Han, Y., Dong, X., Ge, J., Zheng, R., Shi, X., Wang, B., Li, Z., Ren, P., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1020 · Cited by 1,090

Deep learning classifiers for hyperspectral imaging: A review

Paoletti, M. E., Haut, J. M., Plaza, J., & Plaza, A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.09.006 · Cited by 1,144

MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data

Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Stegle, O. (2020) · Genome Biology · doi:10.1186/s13059-020-02015-1 · Cited by 1,091

3D variability analysis: Resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM

Punjani, A., & Fleet, D. J. (2021) · Journal of Structural Biology · doi:10.1016/j.jsb.2021.107702 · Cited by 959

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

Cossarizza, A., Chang, H., Radbruch, A., Acs, A., Adam, D., Adam‐Klages, S., Agace, W. W., Aghaeepour, N., Akdiş, M., Allez, M., et al. (2019) · European Journal of Immunology · doi:10.1002/eji.201970107 · Cited by 1,007

Complex Oscillatory Waves Emerging from Cortical Organoids Model Early Human Brain Network Development

Trujillo, C. A., Gao, R., Negraes, P. D., Gu, J., Buchanan, J., Preissl, S., Wang, A., Wu, W., Haddad, G. G., Chaim, I. A., et al. (2019) · Cell Stem Cell · doi:10.1016/j.stem.2019.08.002 · Cited by 974

Orchestrating single-cell analysis with Bioconductor

Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945

Discovering the anticancer potential of non-oncology drugs by systematic viability profiling

Corsello, S. M., Nagari, R. T., Spangler, R. D., Rossen, J., Kocak, M., Bryan, J. G., Humeidi, R., Peck, D., Wu, X., Tang, A. A., et al. (2020) · Nature Cancer · doi:10.1038/s43018-019-0018-6 · Cited by 878

Single-cell transcriptomics of human T cells reveals tissue and activation signatures in health and disease

Szabo, P. A., Levitin, H. M., Miron, M., Snyder, M. E., Senda, T., Yuan, J., Cheng, Y. L., Bush, E. C., Dogra, P., Thapa, P., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12464-3 · Cited by 756

An integrated cell atlas of the lung in health and disease

Sikkema, L., Ramírez-Suástegui, C., Strobl, D. C., Gillett, T. E., Zappia, L., Madissoon, E., Markov, N. S., Zaragosi, L., Ji, Y., Ansari, M., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02327-2 · Cited by 930

Single-cell multi-omics analysis of the immune response in COVID-19

Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research (CITIID-NIHR) COVID-19 BioResource Collaboration, Stephenson, E., Reynolds, G., Botting, R. A., Calero-Nieto, F. J., Morgan, M. D., Tuong, Z. K., Bach, K., Sungnak, W., Worlock, K. B., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01329-2 · Cited by 742

Trajectory-based differential expression analysis for single-cell sequencing data

den Berge, K. V., de Bézieux, H. R., Street, K., Saelens, W., Cannoodt, R., Saeys, Y., Dudoit, S., & Clement, L. (2020) · Nature Communications · doi:10.1038/s41467-020-14766-3 · Cited by 851

A Python library for probabilistic analysis of single-cell omics data

Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Valiollah Pour Amiri, V., Hong, J., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01206-w · Cited by 789

A human brain vascular atlas reveals diverse mediators of Alzheimer’s risk

Yang, A. C., Vest, R. T., Kern, F., Lee, D. P., Agam, M., Maat, C. A., Losada, P. M., Chen, M. B., Schaum, N., Khoury, N., et al. (2022) · Nature · doi:10.1038/s41586-021-04369-3 · Cited by 797

CellRank for directed single-cell fate mapping

Lange, M., Bergen, V., Klein, M., Setty, M. N., Reuter, B., Bakhti, M., Lickert, H., Ansari, M., Gote-Schniering, J., Schiller, H. B., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01346-6 · Cited by 783

hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data

Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E., & Swarup, V. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100498 · Cited by 773

Peripheral T cell expansion predicts tumour infiltration and clinical response

Wu, T. D., Madireddi, S., de Almeida, P. E., Banchereau, R., Chen, Y.-J. J., Chitre, A. S., Chiang, E. Y., Iftikhar, H., O’Gorman, W. E., Au-Yeung, A., et al. (2020) · Nature · doi:10.1038/s41586-020-2056-8 · Cited by 770

Decoding myofibroblast origins in human kidney fibrosis

Kuppe, C., Ibrahim, M. M., Kranz, J., Zhang, X., Ziegler, S., Perales-Patón, J., Jansen, J., Reimer, K. C., Smith, J. R., Dobie, R., et al. (2021) · Nature · doi:10.1038/s41586-020-2941-1 · Cited by 769

A human cell atlas of fetal gene expression

Cao, J., O’Day, D. R., Pliner, H. A., Kingsley, P. D., Deng, M., Daza, R. M., Zager, M. A., Aldinger, K. A., Blecher-Gonen, R., Zhang, F., et al. (2020) · Science · doi:10.1126/science.aba7721 · Cited by 764

A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors

Slyper, M., Porter, C. B. M., Ashenberg, O., Waldman, J., Drokhlyansky, E., Wakiro, I., Smillie, C. S., Smith-Rosario, G., Wu, J., Dionne, D. A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0844-1 · Cited by 758

Circuits between infected macrophages and T cells in SARS-CoV-2 pneumonia

Grant, R. A., Morales‐Nebreda, L., Markov, N. S., Swaminathan, S., Querrey, M., Rios-Guzman, E., Abbott, D. A., Donnelly, H. K., Donayre, A., Goldberg, I. A., et al. (2021) · Nature · doi:10.1038/s41586-020-03148-w · Cited by 718

Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder

Dong, K., & Zhang, S. (2022) · Nature Communications · doi:10.1038/s41467-022-29439-6 · Cited by 761

Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease

Morabito, S., Miyoshi, E., Michael, N., Shahin, S., Martini, A. C., Head, E., Silva, J., Leavy, K., Perez-Rosendahl, M., & Swarup, V. (2021) · Nature Genetics · doi:10.1038/s41588-021-00894-z · Cited by 643

Molecular topography of an entire nervous system

Taylor, S. R., Santpere, G., Weinreb, A., Barrett, A., Reilly, M. B., Xu, C., Varol, E., Oikonomou, P., Glenwinkel, L., McWhirter, R. D., et al. (2021) · Cell · doi:10.1016/j.cell.2021.06.023 · Cited by 696

Transcription phenotypes of pancreatic cancer are driven by genomic events during tumor evolution

Chan-Seng-Yue, M., Kim, J. C., Wilson, G. W., Ng, K., Figueroa, E. F., O’Kane, G. M., Connor, A. A., Denroche, R. E., Grant, R. C., McLeod, J., et al. (2020) · Nature Genetics · doi:10.1038/s41588-019-0566-9 · Cited by 669

A comparison of automatic cell identification methods for single-cell RNA sequencing data

Abdelaal, T., Michielsen, L., Cats, D., Hoogduin, D., Mei, H., Reinders, M. J. T., & Mahfouz, A. (2019) · Genome Biology · doi:10.1186/s13059-019-1795-z · Cited by 628

Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

Raschka, S., Patterson, J. M., & Nolet, C. (2020) · Information · doi:10.3390/info11040193 · Cited by 928

Cells of the human intestinal tract mapped across space and time

Elmentaite, R., Kumasaka, N., Roberts, K., Fleming, A., Dann, E., King, H. W., Kleshchevnikov, V., Dabrowska, M. H., Pritchard, S., Bolt, L., et al. (2021) · Nature · doi:10.1038/s41586-021-03852-1 · Cited by 706

Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models

Xu, C., Lopez, R., Mehlman, E., Regier, J., Jordan, M. I., & Yosef, N. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209620 · Cited by 691

Onco-fetal Reprogramming of Endothelial Cells Drives Immunosuppressive Macrophages in Hepatocellular Carcinoma

Sharma, A., Seow, J. J. W., Dutertre, C.-A., Pai, R., Blériot, C., Mishra, A., Wong, R. M. M., Singh, G. S. N., Sudhagar, S., Khalilnezhad, S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.040 · Cited by 677

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Fu, Y., Jung, A. W., Viñas, R., González, S., Vöhringer, H. S., Shmatko, A., Yates, L. R., Jimenez‐Liñan, M., Moore, L., & Gerstung, M. (2020) · Nature Cancer · doi:10.1038/s43018-020-0085-8 · Cited by 655

A genome-wide transcriptomic analysis of protein-coding genes in human blood cells

Uhlen, M., Karlsson, M. J., Zhong, W., Tebani, A., Pou, C., Mikes, J., Lakshmikanth, T., Forsström, B., Edfors, F., Odeberg, J., et al. (2019) · Science · doi:10.1126/science.aax9198 · Cited by 594

CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks

Zhong, E. D., Bepler, T., Berger, B., & Davis, J. H. (2021) · Nature Methods · doi:10.1038/s41592-020-01049-4 · Cited by 653

OpenCell: Endogenous tagging for the cartography of human cellular organization

Cho, N. H., Cheveralls, K. C., Brunner, A.-D., Kim, K., Michaelis, A. C., Raghavan, P., Kobayashi, H., Savy, L., Li, J. Y., Canaj, H., et al. (2022) · Science · doi:10.1126/science.abi6983 · Cited by 567

Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets

Belkina, A. C., Ciccolella, C. O., Anno, R., Halpert, R., Špidlen, J., & Snyder-Cappione, J. E. (2019) · Nature Communications · doi:10.1038/s41467-019-13055-y · Cited by 624

Dysregulation of brain and choroid plexus cell types in severe COVID-19

Yang, A. C., Kern, F., Losada, P. M., Agam, M. R., Maat, C. A., Schmartz, G. P., Fehlmann, T., Stein, J. A., Schaum, N., Lee, D. P., et al. (2021) · Nature · doi:10.1038/s41586-021-03710-0 · Cited by 620

Decontamination of ambient RNA in single-cell RNA-seq with DecontX

Yang, S., Corbett, S. E., Koga, Y., Wang, Z., Johnson, W. E., Yajima, M., & Campbell, J. D. (2020) · Genome Biology · doi:10.1186/s13059-020-1950-6 · Cited by 635

Joint probabilistic modeling of single-cell multi-omic data with totalVI

Gayoso, A., Steier, Z., Lopez, R., Regier, J., Nazor, K. L., Streets, A., & Yosef, N. (2021) · Nature Methods · doi:10.1038/s41592-020-01050-x · Cited by 592

Genome-wide CRISPR Screens in Primary Human T Cells Reveal Key Regulators of Immune Function

Shifrut, E., Carnevale, J., Tobin, V., Roth, T. L., Woo, J. M., Bui, C. T., Li, P. J., Diolaiti, M. E., Ashworth, A., & Marson, A. (2018) · Cell · doi:10.1016/j.cell.2018.10.024 · Cited by 573

Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH

Zhang, M., Eichhorn, S. W., Zingg, B., Yao, Z., Cotter, K., Zeng, H., Dong, H., & Zhuang, X. (2021) · Nature · doi:10.1038/s41586-021-03705-x · Cited by 498

Statistical power for cluster analysis

Dalmaijer, E. S., Nord, C. L., & Astle, D. E. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04675-1 · Cited by 710

A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution

Packer, J. S., Zhu, Q., Huynh, C., Sivaramakrishnan, P., Preston, E. A., Dueck, H., Stefanik, D. J., Tan, K., Trapnell, C., Kim, J., et al. (2019) · Science · doi:10.1126/science.aax1971 · Cited by 558

SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis

Wendisch, D., Dietrich, O., Mari, T., von Stillfried, S., Ibarra, I. L., Mittermaier, M., Mache, C., Chua, R. L., Knoll, R., Timm, S., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.033 · Cited by 528

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Rosen, A., Iyer, S. M., Ray, D., Yao, Z., Aspuru‐Guzik, A., Gagliardi, L., Notestein, J. M., & Snurr, R. Q. (2021) · Matter · doi:10.1016/j.matt.2021.02.015 · Cited by 571

Notch signalling drives synovial fibroblast identity and arthritis pathology

Wei, K., Korsunsky, I., Marshall, J. L., Gao, A., Watts, G. F. M., Major, T., Croft, A. P., Watts, J., BLAZAR, P. E., Lange, J. K., et al. (2020) · Nature · doi:10.1038/s41586-020-2222-z · Cited by 536

Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases

Ishigaki, K., Akiyama, M., Kanai, M., Takahashi, A., Kawakami, E., Sugishita, H., Sakaue, S., Matoba, N., Low, S.-K., Okada, Y., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0640-3 · Cited by 508

A noninflammatory mRNA vaccine for treatment of experimental autoimmune encephalomyelitis

Krienke, C., Kolb, L., Diken, E., Streuber, M., Kirchhoff, S., Bukur, T., Akilli-Öztürk, Ö., Kranz, L. M., Berger, H., Petschenka, J., et al. (2021) · Science · doi:10.1126/science.aay3638 · Cited by 520

Single-cell RNA-seq reveals cell type–specific molecular and genetic associations to lupus

Perez, R. K., Gordon, M. G., Subramaniam, M., Kim, M. C., Hartoularos, G. C., Targ, S., Sun, Y., Ogorodnikov, A., Bueno, R., Lu, A. C., et al. (2022) · Science · doi:10.1126/science.abf1970 · Cited by 535

muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data

Crowell, H. L., Soneson, C., Germain, P.-L., Calini, D., Collin, L., Raposo, C., Malhotra, D., & Robinson, M. D. (2020) · Nature Communications · doi:10.1038/s41467-020-19894-4 · Cited by 515

Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices

Wang, A., Murdock, R., Kauwe, S. K., Oliynyk, A. O., Gurlo, A., Brgoch, J., Persson, K. A., & Sparks, T. D. (2020) · Chemistry of Materials · doi:10.1021/acs.chemmater.0c01907 · Cited by 532

Single-cell mutation analysis of clonal evolution in myeloid malignancies

Miles, L. A., Bowman, R. L., Merlinsky, T. R., Csete, I., Ooi, A. T., Durruthy-Durruthy, R., Bowman, M., Famulare, C. A., Patel, M. A., Méndez, P., et al. (2020) · Nature · doi:10.1038/s41586-020-2864-x · Cited by 522

The emergence of transcriptional identity in somatosensory neurons

Sharma, N., Flaherty, K., Lezgiyeva, K., Wagner, D. E., Klein, A. M., & Ginty, D. D. (2020) · Nature · doi:10.1038/s41586-019-1900-1 · Cited by 490

Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia

Granja, J. M., Klemm, S., McGinnis, L. M., Kathiria, A. S., Mezger, A., Corces, M. R., Parks, B., Gars, E., Liedtke, M., Zheng, G. X. Y., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0332-7 · Cited by 498

Integrating spatial gene expression and breast tumour morphology via deep learning

He, B., Bergenstråhle, L., Stenbeck, L., Abid, A., Andersson, A., Borg, Å., Maaskola, J., Lundeberg, J., & Zou, J. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-0578-x · Cited by 507

Blastocyst-like structures generated from human pluripotent stem cells

Yu, L., Wei, Y., Duan, J., Schmitz, D. A., Sakurai, M., Wang, L., Wang, K., Zhao, S., Hon, G. C., & Wu, J. (2021) · Nature · doi:10.1038/s41586-021-03356-y · Cited by 504

A large peptidome dataset improves HLA class I epitope prediction across most of the human population

Sarkizova, S., Klaeger, S., Le, P. M., Li, L. W., Oliveira, G., Keshishian, H., Hartigan, C. R., Zhang, W., Braun, D. A., Ligon, K. L., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0322-9 · Cited by 488

Toroidal topology of population activity in grid cells

Gardner, R. J., Hermansen, E., Pachitariu, M., Burak, Y., Baas, N. A., Dunn, B. A., Moser, M.-B., & Moser, E. I. (2022) · Nature · doi:10.1038/s41586-021-04268-7 · Cited by 428

SNT: a unifying toolbox for quantification of neuronal anatomy

Arshadi, C., Günther, U., Eddison, M., Harrington, K., & Ferreira, T. (2021) · Nature Methods · doi:10.1038/s41592-021-01105-7 · Cited by 462

Single-cell transcriptomic characterization of a gastrulating human embryo

Tyser, R. C. V., Mahammadov, E., Nakanoh, S., Vallier, L., Scialdone, A., & Srinivas, S. (2021) · Nature · doi:10.1038/s41586-021-04158-y · Cited by 491

Myelin dysfunction drives amyloid-β deposition in models of Alzheimer’s disease

Depp, C., Sun, T., Sasmita, A. O., Spieth, L., Berghoff, S. A., Nazarenko, T., Overhoff, K., Steixner-Kumar, A. A., Subramanian, S., Arinrad, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06120-6 · Cited by 493

PLSDB: a resource of complete bacterial plasmids

Galata, V., Fehlmann, T., Backes, C., & Keller, A. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1050 · Cited by 469

Single-cell RNA sequencing reveals functional heterogeneity of glioma-associated brain macrophages

Ochocka, N., Segit, P., Walentynowicz, K. A., Wojnicki, K., Cyranowski, S., Swatler, J., Mieczkowski, J., & Kaminska, B. (2021) · Nature Communications · doi:10.1038/s41467-021-21407-w · Cited by 484

scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses

Wang, J., Ma, A., Chang, Y., Gong, J., Jiang, Y., Qi, R., Wang, C., Fu, H., Ma, Q., & Xu, D. H. (2021) · Nature Communications · doi:10.1038/s41467-021-22197-x · Cited by 459

Artificial intelligence for natural product drug discovery

Mullowney, M. W., Duncan, K., Elsayed, S. S., Garg, N., van der Hooft, J. J. J., Martin, N. I., Meijer, D., Terlouw, B. R., Biermann, F., Blin, K., et al. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00774-7 · Cited by 551

Mapping transcriptomic vector fields of single cells

Qiu, X., Zhang, Y., Martin-Rufino, J. D., Weng, C., Hosseinzadeh, S., Yang, D., Pogson, A. N., Hein, M. Y., Min, K. H., Wang, L., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.045 · Cited by 458

Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions

Andersson, A., Larsson, L., Stenbeck, L., Salmén, F., Ehinger, A., Wu, S. Z., Al-Eryani, G., Roden, D., Swarbrick, A., Borg, Å., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26271-2 · Cited by 404

Illuminating protein space with a programmable generative model

Ingraham, J. B., Baranov, M., Costello, Z., Barber, K. W., Wang, W., Ismail, A., Frappier, V., Lord, D. M., Ng-Thow-Hing, C., Van Vlack, E. R., et al. (2023) · Nature · doi:10.1038/s41586-023-06728-8 · Cited by 444

Massively multiplex chemical transcriptomics at single-cell resolution

Srivatsan, S. R., McFaline-Figueroa, J. L., Ramani, V., Saunders, L., Cao, J., Packer, J., Pliner, H. A., Jackson, D. L., Daza, R. M., Christiansen, L., et al. (2020) · Science · doi:10.1126/science.aax6234 · Cited by 401

Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment

Hwang, W. L., Jagadeesh, K. A., Guo, J. A., Hoffman, H. I., Yadollahpour, P., Reeves, J. W., Mohan, R., Drokhlyansky, E., Van Wittenberghe, N., Ashenberg, O., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01134-8 · Cited by 426

Accurate estimation of cell composition in bulk expression through robust integration of single-cell information

Jew, B., Alvarez, M., Rahmani, E., Miao, Z., Ko, A., Garske, K. M., Sul, J. H., Pietiläinen, K. H., Pajukanta, P., & Halperin, E. (2020) · Nature Communications · doi:10.1038/s41467-020-15816-6 · Cited by 443

Signatures of plasticity, metastasis, and immunosuppression in an atlas of human small cell lung cancer

Chan, J. M., Quintanal-Villalonga, À., Gao, V. R., Xie, Y., Allaj, V., Chaudhary, O., Masilionis, I., Egger, J. V., Chow, A. T., Walle, T., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.09.008 · Cited by 464

Integrated spatial genomics reveals global architecture of single nuclei

Takei, Y., Yun, J., Zheng, S., Ollikainen, N., Pierson, N. G., White, J. A., Shah, S., Thomassie, J., Suo, S., Eng, C.-H. L., et al. (2021) · Nature · doi:10.1038/s41586-020-03126-2 · Cited by 407

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

Prim�rzerlegung in Steinschen Algebren

Förster, O. (1964) · Mathematische Annalen · doi:10.1007/bf01362567 · Cited by 32

�ber unirationale Scharen auf algebraischen Mannigfaltigkeiten

Reich, L. (1966) · Mathematische Annalen · doi:10.1007/bf01364537 · Cited by 4

Algebraic Set Operations, Multifunctions, and Indefinite Integrals

Jovanović, M. V., & Jungić, V. M. (1996) · Mathematics Magazine · doi:10.2307/2691279

On Almost α(Λ, sp)-continuous Multifunctions

Boonpok, C., & Khampakdee, J. (2022) · European Journal of Pure and Applied Mathematics · doi:10.29020/nybg.ejpam.v15i2.4277 · Cited by 5