Multiscale PHATE identifies multimodal signatures of COVID-19
Open accessPublished version of a preprint
Abstract
Abstract The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a coarse graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16-hi,CD66b-lo neutrophil and IFNγ+,GranzymeB+ Th17 cell responses enriched in patients who die. Furthermore, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome.
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
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?
Dimensionality reduction methods such as PHATE and Multiscale PHATE can learn abstract representations of biological and clinical data at different levels of granularity, and have been shown to predict clinical outcomes in people with COVID-19.
“Another approach is dimensionality reduction, including novel methods such as PHATE and Multiscale PHATE, which can learn abstract representations of biological and clinical data at different levels of granularity, and have been shown to predict clinical outcomes, for example, in people with coronavirus disease 2019 (COVID-19) 21,22 .”
From Multimodal biomedical AI (2022) · cites this paper as [22]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Yaşlı Kadınlarda Üreme Sağlığı
Kokanalı, D., & Engin Üstün, Y. (n.d.) · DergiPark (Istanbul University) · Cited by 170,090 · see citing works
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424 · see citing works
LIII. On lines and planes of closest fit to systems of points in space
Karl Pearson, F. R. S. (1901) · The London Edinburgh and Dublin Philosophical Magazine and Journal of Science · doi:10.1080/14786440109462720 · Cited by 13,658 · see citing works
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Belkin, M., & Niyogi, P. (2003) · Neural Computation · doi:10.1162/089976603321780317 · Cited by 7,562 · see citing works
Exploring Network Structure, Dynamics, and Function Using NetworkX
Hagberg, A. A., Schult, D. A., & Swart, P. J. (2008) · Proceedings of the Python in Science Conference · doi:10.25080/tcwv9851 · Cited by 7,477 · see citing works
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · see citing works
Factors associated with COVID-19-related death using OpenSAFELY
Williamson, E. J., Walker, A. J., Bhaskaran, K., Bacon, S., Bates, C., Morton, C. E., Curtis, H. J., Mehrkar, A., Evans, D., Inglesby, P., et al. (2020) · Nature · doi:10.1038/s41586-020-2521-4 · Cited by 6,674 · see citing works
Sex differences in immune responses
Klein, S. L., & Flanagan, K. L. (2016) · Nature Reviews Immunology · doi:10.1038/nri.2016.90 · Cited by 6,370 · see citing works
From Louvain to Leiden: guaranteeing well-connected communities
Traag, V. A., Waltman, L., & van Eck, N. J. (2019) · Scientific Reports · doi:10.1038/s41598-019-41695-z · Cited by 5,635 · see citing works
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 · see citing works
Shuman, D. I., Narang, S. K., Frossard, P., Ortega, A., & Vandergheynst, P. (2013) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2235192 · Cited by 4,421 · see citing works
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356 · see citing works
Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19
Liao, M., Liu, Y., Yuan, J., Wen, Y., Xu, G., Zhao, J., Cheng, L., Li, J., Wang, X., Wang, F., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0901-9 · Cited by 2,614 · see citing works
Coifman, R. R., & Lafon, S. (2006) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2006.04.006 · Cited by 2,380 · see citing works
Single-cell chromatin accessibility reveals principles of regulatory variation
Buenrostro, J. D., Wu, B., Litzenburger, U. M., Ruff, D., Gonzales, M. L., Snyder, M. P., Chang, H. Y., & Greenleaf, W. J. (2015) · Nature · doi:10.1038/nature14590 · Cited by 2,308 · see citing works
Longitudinal analyses reveal immunological misfiring in severe COVID-19
Lucas, C., Wong, P., Klein, J., Castro, T. B. R., Silva, J., Sundaram, M., Ellingson, M. K., Mao, T., Oh, J. E., Israelow, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2588-y · Cited by 2,074 · see citing works
Self-citationThe immunopathology of sepsis and potential therapeutic targets
van der Poll, T., van de Veerdonk, F. L., Scicluna, B. P., & Netea, M. G. (2017) · Nature Reviews Immunology · doi:10.1038/nri.2017.36 · Cited by 1,957 · see citing works
Convergent Antibody Responses to SARS-CoV-2 in Convalescent Individuals
Robbiani, D. F., Gaebler, C., Muecksch, F., Lorenzi, J. C. C., Wang, Z., Cho, A., Agudelo, M., Barnes, C. O., Gazumyan, A., Finkin, S., et al. (2020) · Nature · doi:10.1038/s41586-020-2456-9 · Cited by 1,853 · see citing works
Recovering Gene Interactions from Single-Cell Data Using Data Diffusion
van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A. J., Burdziak, C., Moon, K. R., Chaffer, C. L., Pattabiraman, D., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.061 · Cited by 1,829 · see citing works
Self-citationRobust T Cell Immunity in Convalescent Individuals with Asymptomatic or Mild COVID-19
Sekine, T., Perez-Potti, A., Rivera-Ballesteros, O., Strålin, K., Gorin, J.-B., Olsson, A., Llewellyn-Lacey, S., Kamal, H., Bogdanovic, G., Muschiol, S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.017 · Cited by 1,788 · see citing works
A minimal common outcome measure set for COVID-19 clinical research
Marshall, J. C., Murthy, S., Diaz, J. V., Adhikari, N. K., Angus, D., Arabi, Y. M., Baillie, J. K., Bauer, M. W., Berry, S., Blackwood, B., et al. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30483-7 · Cited by 1,605 · see citing works
Neutrophil extracellular traps in COVID-19
Zuo, Y., Yalavarthi, S., Shi, H., Gockman, K., Zuo, M., Madison, J. A., Blair, C., Weber, A. G., Barnes, B. J., Egeblad, M., et al. (2020) · JCI Insight · doi:10.1172/jci.insight.138999 · Cited by 1,602 · see citing works
Mathew, D., Giles, J. R., Baxter, A. E., Oldridge, D. A., Greenplate, A. R., Wu, J. E., Alanio, C., Kuri-Cervantes, L., Pampena, M. B., D’Andrea, K., et al. (2020) · Science · doi:10.1126/science.abc8511 · Cited by 1,509 · see citing works
Middleton, E. A., He, X., Denorme, F., Campbell, R. A., Ng, D. C., Salvatore, S., Mostyka, M., Baxter-Stoltzfus, A., Borczuk, A., Loda, M. F., et al. (2020) · Blood · doi:10.1182/blood.2020007008 · Cited by 1,482 · see citing works
Sex differences in immune responses that underlie COVID-19 disease outcomes
Takahashi, T., Ellingson, M. K., Wong, P., Israelow, B., Lucas, C., Klein, J., Silva, J., Mao, T., Oh, J. E., Tokuyama, M., et al. (2020) · Nature · doi:10.1038/s41586-020-2700-3 · Cited by 1,459 · see citing works
Self-citationBandura, D. R., Baranov, V. I., Ornatsky, O. I., Antonov, A., Kinach, R., Lou, X., Pavlov, S., Vorobiev, S., Dick, J. E., & Tanner, S. D. (2009) · Analytical Chemistry · doi:10.1021/ac901049w · Cited by 1,295 · see citing works
SLINK: An optimally efficient algorithm for the single-link cluster method
Sibson, R. (1973) · The Computer Journal · doi:10.1093/comjnl/16.1.30 · Cited by 1,219 · see citing works
Age-related changes in lymphocyte development and function
Linton, P. J., & Dorshkind, K. A. (2004) · Nature Immunology · doi:10.1038/ni1033 · Cited by 1,180 · see citing works
Induction and molecular signature of pathogenic TH17 cells
Lee, Y., Awasthi, A., Yosef, N., Quintana, F. J., Xiao, S., Peters, A., Wu, C., Kleinewietfeld, M., Kunder, S., Hafler, D. A., et al. (2012) · Nature Immunology · doi:10.1038/ni.2416 · Cited by 1,174 · see citing works
Visualizing structure and transitions in high-dimensional biological data
Moon, K. R., van Dijk, D., Wang, Z., Gigante, S., Burkhardt, D. B., Chen, W. S., Yim, K., Elzen, A. V. D., Hirn, M. J., Coifman, R. R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0336-3 · Cited by 1,065 · see citing works
Self-citationHeightened Innate Immune Responses in the Respiratory Tract of COVID-19 Patients
Zhou, Z. L., Ren, L., Zhang, L., Zhong, J., Xiao, Y., Jia, Z., Guo, L., Yang, J., Wang, C., Jiang, S., et al. (2020) · Cell Host & Microbe · doi:10.1016/j.chom.2020.04.017 · Cited by 963 · see citing works
A dynamic COVID-19 immune signature includes associations with poor prognosis
Laing, A. G., Lorenc, A., del Molino del Barrio, I., Das, A., Fish, M., Monin, L., Muñoz-Ruiz, M., McKenzie, D. R., Hayday, T. S., Francos-Quijorna, I., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1038-6 · Cited by 946 · see citing works
Splatter: simulation of single-cell RNA sequencing data
Zappia, L., Phipson, B., & Oshlack, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1305-0 · Cited by 905 · see citing works
A subset of neutrophils in human systemic inflammation inhibits T cell responses through Mac-1
Pillay, J., Kamp, V. M., van Hoffen, E., Visser, T., Tak, T., Lammers, J. J., Ulfman, L. H., Leenen, L. P. H., Pickkers, P., & Koenderman, L. (2011) · Journal of Clinical Investigation · doi:10.1172/jci57990 · Cited by 829 · see citing works
Lee, J. S., Park, S.-W., Jeong, H. W., Ahn, J. Y., Choi, S. J., Lee, H., Choi, B., Nam, S. K., Sa, M., Kwon, J., et al. (2020) · Science Immunology · doi:10.1126/sciimmunol.abd1554 · Cited by 805 · see citing works
Extrafollicular B cell responses correlate with neutralizing antibodies and morbidity in COVID-19
Woodruff, M. C., Ramonell, R. P., Nguyen, D. C., Cashman, K. S., Saini, A. S., Haddad, N. S., Ley, A. M., Kyu, S., Howell, J. C., Ozturk, T., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-00814-z · Cited by 791 · see citing works
Skendros, P., Mitsios, A., Chrysanthopoulou, A., Mastellos, D. C., Metallidis, S., Rafailidis, P., Ntinopoulou, M., Sertaridou, E., Tsironidou, V., Tsigalou, C., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci141374 · Cited by 786 · see citing works
A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility
Cusanovich, D. A., Hill, A. J., Aghamirzaie, D., Daza, R. M., Pliner, H. A., Berletch, J. B., Filippova, G. N., Huang, X., Christiansen, L., DeWitt, W. S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.052 · Cited by 759 · see citing works
Critical assessment of automated flow cytometry data analysis techniques
Aghaeepour, N., Finak, G., Hoos, H. H., Mosmann, T. R., Brinkman, R. R., Gottardo, R., & Scheuermann, R. H. (2013) · Nature Methods · doi:10.1038/nmeth.2365 · Cited by 589 · see citing works
Host-Viral Infection Maps Reveal Signatures of Severe COVID-19 Patients
Bost, P., Giladi, A., Liu, Y., Bendjelal, Y., Xu, G., David, E., Blecher-Gonen, R., Cohen, M., Medaglia, C., Li, H., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.006 · Cited by 471 · see citing works
Cannon, M. J., Openshaw, P., & Askonas, B. A. (1988) · The Journal of Experimental Medicine · doi:10.1084/jem.168.3.1163 · Cited by 462 · see citing works
Cutting Edge: Rapid In Vivo Killing by Memory CD8 T Cells
Barber, D. L., Wherry, E. J., & Ahmed, R. (2003) · The Journal of Immunology · doi:10.4049/jimmunol.171.1.27 · Cited by 444 · see citing works
Channappanavar, R., Fett, C., Zhao, J., Meyerholz, D. K., & Perlman, S. (2014) · Journal of Virology · doi:10.1128/jvi.01505-14 · Cited by 411 · see citing works
Zhao, Y., Qin, L., Zhang, P., Li, K., Liang, L., Sun, J., Xu, B., Dai, Y., Li, X., Zhang, C., et al. (2020) · JCI Insight · doi:10.1172/jci.insight.139834 · Cited by 402 · see citing works
Liang, S. C., Long, A. J., Bennett, F., Whitters, M. J., Karim, R., Collins, M. A., Goldman, S. J., Dunussi‐Joannopoulos, K., Williams, C. M. M., Wright, J. F., et al. (2007) · The Journal of Immunology · doi:10.4049/jimmunol.179.11.7791 · Cited by 368 · see citing works
Factors Affecting B-Cell Growth and Differentiation
Kishimoto, T. (1985) · Annual Review of Immunology · doi:10.1146/annurev.iy.03.040185.001025 · Cited by 360 · see citing works
Influenza A inhibits Th17-mediated host defense against bacterial pneumonia in mice.
Kudva, A., Scheller, E. V., Robinson, K. M., Crowe, C. R., Choi, S. M., Slight, S. R., Khader, S. A., Dubin, P. J., Enelow, R. I., Kolls, J. K., et al. (2011) · The Journal of Immunology · doi:10.4049/jimmunol.1002194 · Cited by 346 · see citing works
Delvenne, J. (2019) · Digital Commons@Becker (Washington University School of Medicine) · doi:10.1016/s1473-3099 · Cited by 329 · see citing works
Stronger inflammatory/cytotoxic T-cell response in women identified by microarray analysis
Hewagama, A., Patel, D., Yarlagadda, S., Strickland, F. M., & Richardson, B. C. (2009) · Genes & Immunity · doi:10.1038/gene.2009.12 · Cited by 311 · see citing works
Jones, C. E., & Chan, K. (2002) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/ajrcmb.26.6.4757 · Cited by 289 · see citing works
Robust T cell immunity in convalescent individuals with asymptomatic or mild COVID-19Preprint
Sekine, T., Perez‐Potti, A., Rivera‐Ballesteros, O., Strålin, K., Gorin, J., Olsson, A., Llewellyn‐Lacey, S., Kamal, H., Bogdanović, G., Muschiol, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.29.174888 · Cited by 272 · see citing works
Monneret, G., Finck, M.-E., Venet, F., Debard, A., Bohé, J., Bienvenu, J., & Lepape, A. (2004) · Immunology Letters · doi:10.1016/j.imlet.2004.07.009 · Cited by 250 · see citing works
Sánchez‐Cerrillo, I., Landete, P., Orzaiz, B. A., Sánchez‐Alonso, S., Sánchez-Azofra, A., Marcos‐Jiménez, A., Ávalos, E., Alcaraz‐Serna, A., Santos, I. D. L., Mateu‐Albero, T., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci140335 · Cited by 250 · see citing works
Quantifying the effect of experimental perturbations at single-cell resolution
Burkhardt, D. B., Stanley, J. S., Tong, A., Perdigoto, A. L., Gigante, S. A., Herold, K. C., Wolf, G., Giraldez, A. J., van Dijk, D., & Krishnaswamy, S. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-00803-5 · Cited by 226 · see citing works
Self-citationConditional density-based analysis of T cell signaling in single-cell data
Krishnaswamy, S., Spitzer, M. H., Mingueneau, M., Bendall, S. C., Litvin, O., Stone, E., Pe’er, D., & Nolan, G. P. (2014) · Science · doi:10.1126/science.1250689 · Cited by 225 · see citing works
Self-citationMinimum Spanning Tree Based Clustering Algorithms
Grygorash, O., Zhou, Y., & Jorgensen, Z. (2006) · Proceedings - International Conference on Tools with Artificial Intelligence, TAI · doi:10.1109/ictai.2006.83 · Cited by 215 · see citing works
Moskophidis, D., & Kioussis, D. (1998) · The Journal of Experimental Medicine · doi:10.1084/jem.188.2.223 · Cited by 213 · see citing works
Brummelman, J., Haftmann, C., Núñez, N. G., Alvisi, G., Mazza, E. M. C., Becher, B., & Lugli, E. (2019) · Nature Protocols · doi:10.1038/s41596-019-0166-2 · Cited by 196 · see citing works
SARS–CoV-2–specific T cell responses and correlations with COVID-19 patient predisposition
Sattler, A., Angermair, S., Stockmann, H., Heim, K. M., Khadzhynov, D., Treskatsch, S., Halleck, F., Kreis, M. E., & Kotsch, K. (2020) · Journal of Clinical Investigation · doi:10.1172/jci140965 · Cited by 196 · see citing works
Neidleman, J., Luo, X., Frouard, J., Xie, G., Gill, G., Stein, E. S., McGregor, M., Ma, T., George, A. F., Kosters, A., et al. (2020) · Cell Reports Medicine · doi:10.1016/j.xcrm.2020.100081 · Cited by 188 · see citing works
Corrie, B., Marthandan, N., Zimonja, B., Jaglale, J., Zhou, Y., Barr, E., Knoetze, N., Breden, F. M. W., Christley, S., Scott, J. K., et al. (2018) · Immunological Reviews · doi:10.1111/imr.12666 · Cited by 167 · see citing works
Adaptive immune responses to SARS-CoV-2 infection in severe versus mild individuals
Zhang, F., Gan, R., Zhen, Z., Hu, X., Li, X., Zhou, F., Liu, Y., Chen, C., Xie, S., Zhang, B., et al. (2020) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00263-y · Cited by 163 · see citing works
Fortunati, E., Kazemier, K. M., Grutters, J. C., Koenderman, L., & den Bosch, V. J. M. M. V. (2008) · Clinical & Experimental Immunology · doi:10.1111/j.1365-2249.2008.03791.x · Cited by 155 · see citing works
Nolan, S., Vignali, M., Klinger, M., Dines, J. N., Kaplan, I. M., Svejnoha, E., Craft, T., Boland, K., Pesesky, M., Gittelman, R., et al. (2020) · Research Square · doi:10.21203/rs.3.rs-51964/v1 · Cited by 151 · see citing works
Reappearance of effector T cells is associated with recovery from COVID-19
Odak, I., Barros‐Martins, J., Bošnjak, B., Stahl, K., David, S., Wiesner, O., Busch, M., Hoeper, M. M., Pink, I., Welte, T., et al. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.102885 · Cited by 134 · see citing works
Manifold learning-based methods for analyzing single-cell RNA-sequencing data
Moon, K. R., Stanley, J. S., Burkhardt, D. B., van Dijk, D., Wolf, G., & Krishnaswamy, S. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.12.008 · Cited by 127 · see citing works
Self-citationOn the performance of bisecting K-means and PDDP
Savaresi, S. M., & Boley, D. L. (2001) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972719.5 · Cited by 126 · see citing works
Winkler, M. S., Rissiek, A., Priefler, M., Schwedhelm, E., Robbe, L., Bauer, A., Zahrte, C., Zoellner, C., Kluge, S., & Nierhaus, A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0182427 · Cited by 124 · see citing works
Liu, R., Lauridsen, H. M., Amezquita, R. A., Pierce, R. W., Jane‐wit, D., Fang, C., Pellowe, A. S., Kirkiles-Smith, N. C., Gonzalez, A. L., & Pober, J. S. (2016) · The Journal of Immunology · doi:10.4049/jimmunol.1600138 · Cited by 122 · see citing works
Biasi, S. D., Tartaro, D. L., Meschiari, M., Gibellini, L., Bellinazzi, C., Borella, R., Fidanza, L., Mattioli, M., Paolini, A., Gozzi, L., et al. (2020) · European Journal of Immunology · doi:10.1002/eji.202048838 · Cited by 116 · see citing works
Hynninen, M. S., Pettilä, V., Takkunen, O. S., Orko, R., Jansson, S., Kuusela, P., Renkonen, R., & Valtonen, M. (2003) · Shock · doi:10.1097/01.shk.0000068322.08268.b4 · Cited by 113 · see citing works
Lee, J., Tam, H., Adler, L., Ilstad-Minnihan, A., Macaubas, C., & Mellins, E. (2017) · PLoS ONE · doi:10.1371/journal.pone.0183594 · Cited by 107 · see citing works
Sodhi, C. P., Nguyen, J. M., Yamaguchi, Y., Werts, A. D., Lu, P., Ladd, M. R., Fulton, W. B., Kovler, M. L., Wang, S., Prindle, T., et al. (2019) · The Journal of Immunology · doi:10.4049/jimmunol.1900579 · Cited by 102 · see citing works
Self-citationADAM17 cleaves CD16b (FcγRIIIb) in human neutrophils
Wang, Y., Wu, J., Newton, R. C., Bahaie, N. S., Long, C., & Walcheck, B. K. (2012) · Biochimica et Biophysica Acta (BBA) - Molecular Cell Research · doi:10.1016/j.bbamcr.2012.11.027 · Cited by 95 · see citing works
Aberrant hyperactivation of cytotoxic T-cell as a potential determinant of COVID-19 severity
Kang, C. K., Han, G.-C., Kim, M., Kim, G., Shin, H. M., Song, K., Choe, P. G., Park, W. B., Kim, E. S., Kim, H. B., et al. (2020) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2020.05.106 · Cited by 81 · see citing works
Heterogeneity Among Neutrophils
Garley, M., & Jabłońska, E. (2017) · Archivum Immunologiae et Therapiae Experimentalis · doi:10.1007/s00005-017-0476-4 · Cited by 71 · see citing works
Woodruff, M. C., Ramonell, R. P., Cashman, K. S., et al. (2020) · medRxiv · doi:10.1101/2020.04.29.20083717 · Cited by 61 · see citing works
Activation of the Granzyme Pathway in Children With Severe Respiratory Syncytial Virus Infection
Reinout, A., Bos, A. P., Bots, M. L., Wolbink, A. M., van Ham, S. M., Medema, J. P., Lutter, R., & van Woensel, J. B. M. (2008) · Pediatric Research · doi:10.1203/pdr.0b013e31816fdc32 · Cited by 52 · see citing works
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 · see citing works
Self-citationClinical characteristics and outcomes for 7,995 patients with SARS-CoV-2 infection
McPadden, J., Warner, F. J., Young, H. P., Hurley, N. C., Pulk, R., Singh, A., Durant, T. J. S., Gong, G., Desai, N. R., Haimovich, A. D., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0243291 · Cited by 47 · see citing works
Self-citationHierarchical data organization, clustering and denoising via localized diffusion folders
David, G., & Averbuch, A. Z. (2011) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2011.09.002 · Cited by 46 · see citing works
Leeb, W., & Coifman, R. R. (2015) · Journal of Fourier Analysis and Applications · doi:10.1007/s00041-015-9439-5 · Cited by 22 · see citing works
Clinical Characteristics and Outcomes for 7,995 Patients with SARS-CoV-2 InfectionPreprint
McPadden, J., Warner, F. J., Young, H. P., et al. (2020) · medRxiv · doi:10.1101/2020.07.19.20157305 · Cited by 21 · see citing works
Coarse Graining of Data via Inhomogeneous Diffusion Condensation
Brugnone, N. G., Gonopolskiy, A., Moyle, M. W., Kuchroo, M. R., van Dijk, D., Moon, K. R., Colón‐Ramos, D. A., Wolf, G., Hirn, M., & Krishnaswamy, S. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006013 · Cited by 18 · see citing works
Self-citationMarshall, N. F., & Hirn, M. (2017) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2017.11.003 · Cited by 17 · see citing works
Self-citationInterplay of Monocytes and T Lymphocytes in COVID-19 SeverityPreprint
Padgett, L. E., Dinh, H. Q., Chee, S., Olingy, C. E., Wu, R., Araujo, D. J., Vijayanand, P., Ottensmeier, C. H. H., & Hedrick, C. C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.17.209304 · Cited by 12 · see citing works
Visualizing High Dimensional Dynamical Processes
Duque, A. F., Wolf, G., & Moon, K. R. (2019) · IEEE International Workshop on Machine Learning for Signal Processing (MLSP) · doi:10.1109/mlsp.2019.8918875 · Cited by 11 · see citing works
Self-citationCover-based bounds on the numerical rank of Gaussian kernels
Bermanis, A., Wolf, G., & Averbuch, A. Z. (2013) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2013.05.004 · Cited by 10 · see citing works
Self-citationCoarse-grained localized diffusion
Wolf, G., Rotbart, A., David, G., & Averbuch, A. Z. (2012) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2012.02.004 · Cited by 6 · see citing works
Self-citationGigante, S., Stanley, J. S., Vu, N., van Dijk, D., Moon, K. R., Wolf, G., & Krishnaswamy, S. (2019) · International Conference on Sampling Theory and Applications · doi:10.1109/sampta45681.2019.9030994 · Cited by 4 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- [41]Gigante S, Charles AS, Krishnaswamy S. & Mishne G. Visualizing the phate of neural networks. In Advances in Neural Information Processing Systems 32 (eds Wallach H, Larochelle, Beygelzimer A, d’Alché-Buc F, Fox E. & Garnett R) (Curran Associates, Inc., 2019).
Cited by
Sorted by citations
Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365 · see citing works
The first 12 months of COVID-19: a timeline of immunological insights
Carvalho, T., Krammer, F., & Iwasaki, A. (2021) · Nature reviews. Immunology · doi:10.1038/s41577-021-00522-1 · Cited by 448 · see citing works
Self-citationBogaert, D., van Beveren, G. J., de Koff, E. M., Parga, P. L., Lopez, C. B., Koppensteiner, L., Clerc, M., Hasrat, R., Arp, K., Chu, M. L. J. N., et al. (2023) · Cell Host & Microbe · doi:10.1016/j.chom.2023.01.018 · Cited by 248 · see citing works
Domestic dog lineages reveal genetic drivers of behavioral diversification
Dutrow, E. V., Serpell, J. A., & Ostrander, E. A. (2022) · Cell · doi:10.1016/j.cell.2022.11.003 · Cited by 94 · see citing works
Mapping Phenotypic Plasticity upon the Cancer Cell State Landscape Using Manifold Learning
Burkhardt, D. B., Juan, B. P. S., Lock, J. G., Krishnaswamy, S., & Chaffer, C. L. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0282 · Cited by 70 · see citing works
Self-citationHe, S., Jin, Y., Nazaret, A., Shi, L., Chen, X., Rampersaud, S., Dhillon, B. S., Valdez, I. K., Friend, L. E., Fan, J. L., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02173-8 · Cited by 76 · see citing works
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 57 · see citing works
Self-citationBrown, B., Ojha, V., Fricke, I., Al-Sheboul, S. A., Imarogbe, C., Gravier, T., Green, M., Peterson, L., Koutsaroff, I. P., Demir, A. N., et al. (2023) · Vaccines · doi:10.3390/vaccines11020408 · Cited by 54 · see citing works
Multi-omic analysis of Huntington’s disease reveals a compensatory astrocyte state
Paryani, F., Kwon, J., Ng, C., Jakubiak, K. A., Madden, N., Ofori, K., Tang, A., Lü, H., Xia, S., Li, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50626-0 · Cited by 49 · see citing works
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 · see citing works
Self-citationJoodaki, 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 34 · see citing works
A diminished immune response underlies age-related SARS-CoV-2 pathologies
Oishi, K., Horiuchi, S., Frere, J. J., Schwartz, R. E., & tenOever, B. R. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111002 · Cited by 31 · see citing works
Fuda, F. S., Chen, M., Chen, W., & Cox, A. M. (2023) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2023.02.004 · Cited by 27 · see citing works
Heterogeneity in IgG‐CD16 signaling in infectious disease outcomes*
González, J. C., Chakraborty, S., Thulin, N. K., & Wang, T. T. (2022) · Immunological Reviews · doi:10.1111/imr.13109 · Cited by 23 · see citing works
HSV-2 triggers upregulation of MALAT1 in CD4+ T cells and promotes HIV latency reversal
Pierce, C. A., Loh, L. N., Steach, H. R., Cheshenko, N., Preston‐Hurlburt, P., Zhang, F., Stransky, S., Kravets, L., Sidoli, S., Philbrick, W. M., et al. (2023) · Journal of Clinical Investigation · doi:10.1172/jci164317 · Cited by 23 · see citing works
Self-citationLiu, C., Amodio, M., Shen, L. L., Gao, F., Avesta, A., Aneja, S., Wang, J. C., Priore, L. V. D., & Krishnaswamy, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72111-3_15 · Cited by 23 · see citing works
Self-citationSingle-cell spatial transcriptomics in cardiovascular development, disease, and medicine
Han, S., Xu, Q., Du, Y., Tang, C., Cui, H., Xia, X., Zheng, R., Sun, Y., & Shang, H. (2023) · Genes & Diseases · doi:10.1016/j.gendis.2023.101163 · Cited by 22 · see citing works
Dissecting CD8+ T cell pathology of severe SARS-CoV-2 infection by single-cell immunoprofiling
Schreibing, F., Hannani, M. T., Kim, H., Nagai, J. S., Ticconi, F., Fewings, E., Bleckwehl, T., Begemann, M., Torow, N., Kuppe, C., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1066176 · Cited by 20 · see citing works
Toward a mathematical theory of trajectory inference
Lavenant, H., Zhang, S. X., Kim, Y., & Schiebinger, G. R. (2024) · The Annals of Applied Probability · doi:10.1214/23-aap1969 · Cited by 44 · see citing works
Joint representation and visualization of derailed cell states with Decipher
Nazaret, A., Fan, J. L., Lavallée, V., Burdziak, C., Cornish, A. E., Kiseliovas, V., Bowman, R. L., Masilionis, I., Chun, J., Eisman, S. E., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03682-8 · Cited by 18 · see citing works
Yi, H., Plotkin, A. L., & Stanley, N. (2024) · Genome biology · doi:10.1186/s13059-023-03143-0 · Cited by 19 · see citing works
Tornini, V. A., Lee, H., Miao, L., Tang, Y., Dube, S. E., Gerson, T., Schmidt, V., Du, K., Kuchroo, M., Kroll, F., et al. (2023) · eLife · doi:10.7554/elife.82249 · Cited by 18 · see citing works
Self-citationBuilding and analyzing metacells in single-cell genomics data
Bilous, M., Hérault, L., Gabriel, A. A., Teleman, M., & Gfeller, D. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00045-6 · Cited by 18 · see citing works
Verschoor, C. P., Picard, E., Andrew, M. K., Haynes, L., Loeb, M. B., Pawelec, G., & Kuchel, G. A. (2023) · Frontiers in Aging · doi:10.3389/fragi.2022.1098200 · Cited by 16 · see citing works
Towards a mathematical theory of trajectory inferencePreprint
Lavenant, H., Zhang, S. X., Kim, Y., & Schiebinger, G. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.09204 · Cited by 16 · see citing works
HUMAP: Hierarchical Uniform Manifold Approximation and Projection
Marcílio-Jr, W. E., Eler, D. M., Paulovich, F. V., & Martins, R. M. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3471181 · Cited by 16 · see citing works
Single‐Cell and Spatial Omics: Methods and Applications
Cen, X., Huang, X., Deng, E., Gong, X., Tan, N., Ye, J. T., Wang, Y., Eils, R., Luo, Q., Li, Y., et al. (2026) · MedComm · doi:10.1002/mco2.70713 · Cited by 13 · see citing works
A unique cytotoxic CD4 + T cell‐signature defines critical COVID‐19
Baird, S., Ashley, C. L., Marsh‐Wakefield, F., Alca, S., Ashhurst, T. M., Ferguson, A. L., Lukeman, H., Counoupas, C., Post, J. J., Koneçny, P., et al. (2023) · Clinical & Translational Immunology · doi:10.1002/cti2.1463 · Cited by 13 · see citing works
Learning Shared Neural Manifolds from Multi-Subject FMRI Data
Huang, J., Busch, E. L., Wallenstein, T., Gerasimiuk, M., Benz, A., Lajoie, G., Wolf, G., Turk‐Browne, N. B., & Krishnaswamy, S. (2022) · Machine learning for signal processing ... · doi:10.1109/mlsp55214.2022.9943383 · Cited by 13 · see citing works
Self-citationZhou, Y., Khasentino, J., Yun, T., Biradar, M. I., Shreibati, J. B., Lai, D., Schwantes-An, T.-H. L., Luben, R., McCaw, Z. R., Engmann, J., et al. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.05.015 · Cited by 16 · see citing works
Steinwand, S., Hörndli, C. S., Ferris, E., Emery, J., Murcia, J. D. G., Rodriguez, A. C., Leydsman, T. C., Chaix, A., Thomas, A., Davey, C. F., et al. (2025) · Science · doi:10.1126/science.adp4701 · Cited by 11 · see citing works
A Critical Analysis of the Usage of Dimensionality Reduction in Four Domains
Cashman, D., Keller, M. S., Jeon, H., Kwon, B. C., & Wang, Q. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3567989 · Cited by 11 · see citing works
Biases in machine-learning models of human single-cell data
Willem, T., Shitov, V. A., Luecken, M. D., Kilbertus, N., Bauer, S., Piraud, M., Buyx, A., & Theis, F. J. (2025) · Nature Cell Biology · doi:10.1038/s41556-025-01619-8 · Cited by 11 · see citing works
Diffusive topology preserving manifold distances for single-cell data analysis
Wei, J., Zhang, B., Wang, Q., Zhou, T., Tian, T., & Chen, L. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2404860121 · Cited by 8 · see citing works
Venkat, A., Bhaskar, D., & Krishnaswamy, S. (2023) · Trends in Immunology · doi:10.1016/j.it.2023.05.003 · Cited by 7 · see citing works
Self-citationDetecting global and local hierarchical structures in cell-cell communication using CrossChat
Wang, X., Almet, A. A., & Nie, Q. H. (2024) · Nature Communications · doi:10.1038/s41467-024-54821-x · Cited by 8 · see citing works
Metacells untangle large and complex single-cell transcriptome networksPreprint
Bilous, M., Tran, L., Cianciaruso, C., Gabriel, A. A., Michel, H., Carmona, S. J., Pittet, M. J., & Gfeller, D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.07.447430 · Cited by 8 · see citing works
Starfysh reveals heterogeneous spatial dynamics in the breast tumor microenvironmentPreprint
He, S., Jin, Y., Nazaret, A., Shi, L., Chen, X., Rampersaud, S., Dhillon, B. S., Valdez, I. K., Friend, L. E., Fan, J. L., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.21.517420 · Cited by 7 · see citing works
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. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.16.520739 · Cited by 7 · see citing works
Young, J., Inamo, J., Caterer, Z., Krishna, R., & Zhang, F. (2025) · Advancement of science · doi:10.1002/advs.202503289 · Cited by 8 · see citing works
Expanding the diagnostic toolbox for complex genetic immune disorders
Abrams, E. D., Basu, A., Thomas, M. E. Z., Henrickson, S. E., & Abraham, R. S. (2024) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2024.11.022 · Cited by 5 · see citing works
Analyzing network diversity of cell–cell interactions in COVID-19 using single-cell transcriptomics
Wang, X., Almet, A. A., & Nie, Q. H. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.948508 · Cited by 5 · see citing works
The Manifold Scattering Transform for High-Dimensional Point Cloud Data
Chew, J., Steach, H. R., Viswanath, S., Wu, H., Hirn, M., Needell, D. M., Krishnaswamy, S., & Perlmutter, M. (2022) · PubMed · doi:10.48550/arxiv.2206.10078 · Cited by 5 · see citing works
Self-citationMulti-OMIC analysis of Huntington disease reveals a neuroprotective astrocyte statePreprint
Paryani, F., Kwon, J., Ng, C. W., Madden, N., Ofori, K., Tang, A., Lü, H., Li, J., Mahajan, A., Davidson, S. M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.08.556867 · Cited by 4 · see citing works
Connecting chemical structure to single cell signaling profiles
Thirman, H. L., Hayes, M., Geben, L. C., Ihrie, R. A., Brown, L. E., Porco, J. A. J., & Irish, J. M. (2025) · Communications Biology · doi:10.1038/s42003-025-08545-3 · Cited by 3 · see citing works
Interferon lambda as a potential treatment for COVID-19
McCarthy, M. W. (2023) · Expert Opinion on Biological Therapy · doi:10.1080/14712598.2023.2211709 · Cited by 3 · see citing works
Mapping the gene space at single-cell resolution with gene signal pattern analysis
Venkat, A., Leone, S., Youlten, S. E., Fagerberg, E., Attanasio, J., Joshi, N. S., Perlmutter, M., & Krishnaswamy, S. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00734-0 · Cited by 5 · see citing works
Self-citationGazeau, S., Deng, X., Ooi, H. K., Mostefai, F., Hussin, J., Heffernan, J. M., Jenner, A. L., & Craig, M. (2023) · ImmunoInformatics · doi:10.1016/j.immuno.2023.100021 · Cited by 3 · see citing works
Self-citationTime-Inhomogeneous Diffusion Geometry and Topology
Huguet, G., Tong, A., Rieck, B., Huang, J., Kuchroo, M. R., Hirn, M., Wolf, G., & Krishnaswamy, S. (2023) · SIAM Journal on Mathematics of Data Science · doi:10.1137/21m1462945 · Cited by 3 · see citing works
Self-citationGaining biological insights through supervised data visualization
Rhodes, J. S., Aumon, A., Morin, S., Girard, M., Larochelle, C., Brunet‐Ratnasingham, E., Pagliuzza, A., Marchitto, L., Zhang, W., Cutler, A., et al. (2026) · Nature Computational Science · doi:10.1038/s43588-026-00999-7 · Cited by 3 · see citing works
Self-citationToward the novel AI tasks in infection biology
Yakimovich, A. (2024) · mSphere · doi:10.1128/msphere.00591-23 · Cited by 4 · see citing works
Data-driven approaches for genetic characterization of SARS-CoV-2 lineagesPreprint
Mostefai, F., Gamache, I., Huang, J., N’Guessan, A., Pelletier, J., Pesaranghader, A., Hamelin, D. J., Murall, C. L., Poujol, R., Grenier, J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.28.462270 · Cited by 3 · see citing works
Self-citationUtilizing multimodal AI to improve genetic analyses of cardiovascular traitsPreprint
Zhou, Y., Cosentino, J., Yun, T., Biradar, M. I., Shreibati, J. B., Lai, D., Schwantes-An, T.-H. L., Luben, R., McCaw, Z. R., Engmann, J., et al. (2024) · medRxiv · doi:10.1101/2024.03.19.24304547 · Cited by 3 · see citing works
QOT: Quantized Optimal Transport for sample-level distance matrix in single-cell omics
Wang, Z., Zhan, Q., Yang, S., Mu, S., Chen, J., Garai, S., Orzechowski, P., Wagenaar, J., & Shen, L. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae713 · Cited by 2 · see citing works
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 · see citing works
Velociraptor: Cross-Platform Quantitative Search Using Hallmark Cell FeaturesPreprint
Cross, C. E., Mayeda, C., Medina, S., Hayes, M., Kaviany, S., Connelly, J. A., Rathmell, J. C., Weaver, K. D., Thompson, R. C., Chambless, L. B., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.01.591375 · Cited by 2 · see citing works
Young, J., Inamo, J., Caterer, Z., Krishna, R., & Zhang, F. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.24.634132 · Cited by 2 · see citing works
Sittipo, P., Park, J.-Y., Tiffany, E., Oh, A., Moon, S., Lee, C. H., Oh, J. S., Kim, T.-Y., Kweon, M., Choi, J., et al. (2026) · Experimental & Molecular Medicine · doi:10.1038/s12276-026-01779-z · Cited by 1 · see citing works
Self-citationMIA: A visual analytics system for multimodal spectral imaging data
Rave, H., Kronenberg, K., Gödde, H., Tobergte, L., Holtkamp, M., Werner, J., Bohrer, P., Lohöfer, F. K., Braren, R., Clases, D., et al. (2026) · Computers & Graphics · doi:10.1016/j.cag.2026.104727 · Cited by 1 · see citing works
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 1 · see citing works
Karwański, M., Grzybowska, U., Mierzejewska, E., & Szamotulska, K. (2024) · BMC Public Health · doi:10.1186/s12889-024-18355-7 · Cited by 1 · see citing works
Exploring Hierarchical Structures of Cell Types in scRNA-seq Data
Zhai, H., Ye, Y., Hu, Y., Wang, L., & Gao, L. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5131-0_1 · Cited by 1 · see citing works
Geometric quantification of cell phenotype transition manifolds with information geometry
Huang, M., Wang, Y., Ying, J., Xiao, H., HaiJun, Z. H. O. U., Zhang, L., & Wang, W. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101564 · Cited by 2 · see citing works
Casals-Franch, R., Villà-Freixa, J., Nonell, L., & de Lomana, A. L. G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.15.682506 · Cited by 1 · see citing works
Spatiotemporal coordination of stem cell behavior following alveolar injuryPreprint
Chioccioli, M., Magruder, S., McDonough, J. E., Nouws, J., Yang, T., Gonzalez, D. G., Borriello, L., Traub, B. C., Ye, X., Hendry, C., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.28.514255
Self-citationInterpretation and Hierarchical methods for Dimensionality Reduction
Marcílio-Jr, W. E., & Eler, D. M. (2023) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.5753/sibgrapi.est.2023.27456
Victoria-Castro, A., Arora, T., Simonov, M., Biswas, A., Alausa, J., Subair, L., Gerber, B. A., Nguyen, A. T., Hsiao, A. L., Hintz, R., et al. (2023) · JMIR Formative Research · doi:10.2196/52120
Self-citationMedical Physics in Practice: Tools, Techniques, and Case Studies
Rashid, L. A. K., Sadiq, F. S. W., miran, K. A. B., Jabbar, M. A. Y., Mahdi, M. J. S., & Dhaif, Z. J. Y. (2025) · Bright Sky Publications eBooks · doi:10.62906/bs.book.352
Chen, S., Gao, H., Zhou, J., Cui, M., Tian, X., Sun, W., Lai, S., Qin, Y., & Jia, X. (2026) · Animal Microbiome · doi:10.1186/s42523-026-00622-0
Data Visualization using Functional Data Analysis
Chen, H., Correa, A. F. D., Wolf, G., & Moon, K. R. (2025) · International Conference on Sampling Theory and Applications · doi:10.1109/sampta64769.2025.11133502
Self-citationDissecting sequence determinants of DNA methylation and in silico perturbationPreprint
Chen, F., Zhang, Q., Zhu, S., Liang, J., Zhong, S., Zhang, Z., Su, J., Guo, Y., Fan, X., Zhang, Y., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.20.689274
Chen, R., Bartelo, N., Arikatla, M. R., Mason, C. E., & Elemento, O. (2025) · medRxiv · doi:10.1101/2025.11.27.25341167
Karwański, M., Grzybowska, U., Mierzejewska, E., & Szamotulska, K. (2024) · Figshare · doi:10.6084/m9.figshare.26707059
Yi, H., Plotkin, A. L., & Stanley, N. (2024) · Figshare · doi:10.6084/m9.figshare.26659990
Yi, H., Plotkin, A. L., & Stanley, N. (2024) · Figshare · doi:10.6084/m9.figshare.26659993
A computationally efficient hybrid model for enhancing quality clusters in visualization
Kuruva, H., Narthu, C., Swarupa Rani, K., & Moiz, S. A. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110772
Zheng, Z., Huang, J., Yu, S., & Yu, Z. (2026) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.25-00111r2
Self-citation
- Authors
- Kuchroo, M., Huang, J., Wong, P., Grenier, J.-C., Shung, D., Tong, A., Lucas, C., Klein, J., Burkhardt, D. B., Gigante, S., Godavarthi, A., Rieck, B., Israelow, B., Simonov, M., Mao, T., Oh, J. E., Silva, J., Takahashi, T., Odio, C. D., Casanovas-Massana, A., Fournier, J., Yale IMPACT Team, Obaid, A., Moore, A., Lu-Culligan, A., Nelson, A., Brito, A., Nunez, A., Martin, A., Wyllie, A. L., Watkins, A., Park, A., Venkataraman, A., Geng, B., Kalinich, C., Vogels, C. B. F., Harden, C., Todeasa, C., Jensen, C., Kim, D., McDonald, D., Shepard, D., Courchaine, E., White, E. B., Song, E., Silva, E., Kudo, E., DeIuliis, G., Wang, H., Rahming, H., Park, H.-J., Matos, I., Ott, I. M., Nouws, J., Valdez, J., Fauver, J., Lim, J., Rose, K.-A., Anastasio, K., Brower, K., Glick, L., Sharma, L., Sewanan, L., Knaggs, L., Minasyan, M., Batsu, M., Tokuyama, M., Muenker, M. C., Petrone, M., Kuang, M., Nakahata, M., Campbell, M., Linehan, M., Askenase, M. H., Simonov, M., Smolgovsky, M., Grubaugh, N. D., Sonnert, N., Naushad, N., Vijayakumar, P., Lu, P., Earnest, R., Martinello, R., Herbst, R., Datta, R., Handoko, R., Bermejo, S., Lapidus, S., Prophet, S., Bickerton, S., Velazquez, S., Mohanty, S., Alpert, T., Rice, T., Schulz, W., Khoury-Hanold, W., Peng, X., Yang, Y., Cao, Y., Strong, Y., Farhadian, S., Dela Cruz, C. S., Ko, A. I., Hirn, M. J., Wilson, F. P., Hussin, J. G., Wolf, G., Iwasaki, A., & Krishnaswamy, S.
- Affiliations
- Yale University (US)
- Montreal Heart Institute (CA)
- ETH Zurich (CH)
- Yale New Haven Hospital (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Krishnaswamy, Smita
- ORCID
- Kuchroo, Manik
- Wong, Patrick
- Grenier, Jean-Christophe
- Shung, Dennis
- Tong, Alexander
- Lucas, Carolina
- Klein, Jon
- Burkhardt, Daniel B.
- Gigante, Scott
- Rieck, Bastian
- Israelow, Benjamin
- Mao, Tianyang
- Oh, Ji Eun
- Takahashi, Takehiro
- Odio, Camila D.
- Fournier, John
- Obaid, Abeer
- Moore, Adam
- Lu-Culligan, Alice
- Brito, Anderson
- Martin, Anjelica
- Wyllie, Anne L.
- Park, Annsea
- Venkataraman, Arvind
- Geng, Bertie
- Kalinich, Chaney
- Vogels, Chantal B. F.
- Harden, Christina
- Kim, Daniel
- White, Elizabeth B.
- Song, Eric
- Silva, Erin
- Kudo, Eriko
- DeIuliis, Giuseppe
- Wang, Haowei
- Park, Hong-Jai
- Ott, Isabel M.
- Nouws, Jessica
- Valdez, Jordan
- Fauver, Joseph
- Lim, Joseph
- Rose, Kadi-Ann
- Glick, Laura
- Sewanan, Lorenzo
- Tokuyama, Maria
- Petrone, Mary
- Campbell, Melissa
- Linehan, Melissa
- Askenase, Michael H.
- Grubaugh, Nathan D.
- Sonnert, Nicole
- Vijayakumar, Pavithra
- Lu, Peiwen
- Earnest, Rebecca
- Herbst, Roy
- Datta, Rupak
- Handoko, Ryan
- Bermejo, Santos
- Lapidus, Sarah
- Prophet, Sarah
- Bickerton, Sean
- Velazquez, Sofia
- Mohanty, Subhasis
- Alpert, Tara
- Rice, Tyler
- Schulz, Wade
- Khoury-Hanold, William
- Peng, Xiaohua
- Yang, Yexin
- Cao, Yiyun
- Farhadian, Shelli
- Ko, Albert I.
- Hirn, Matthew J.
- Wilson, F. Perry
- Hussin, Julie G.
- Wolf, Guy
- Iwasaki, Akiko
- Krishnaswamy, Smita
- Venue
- Nature Biotechnology
- Published
- 2022-02-28
- Volume
- 40
- Issue
- 5
- Pages
- 681-691
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC10015653
- OpenAlex
- W4214544930
- Cited by
- 72
- References
- 90
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 21 · 2023: 16 · 2022: 12 · 2021: 2
- FWCI
- 2.40
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Biotechnology (publishedVersion)
- SSRN Electronic Journal (publishedVersion)
- Research Square (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Howard Hughes Medical Institute
- Yale University
- Canadian Institute for Advanced Research
- Strong
- Fondation Institut de Cardiologie de Montréal
- Institut de Cardiologie de Montréal
- Institut de Valorisation des Données
- National Institutes of Health (1f30ai157270-01, UL1TR001863)
- Agency for Healthcare Research and Quality (R01HS027626)
- Natural Sciences and Engineering Research Council of Canada (03267)
- National Institute of Mental Health (K23MH118999)
- National Institute of General Medical Sciences (R01GM130847, R01GM135929)
- National Institute of Allergy and Infectious Diseases (F30 AI157270, R01AI157488, U19AI089992, T32AI007517)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK113191, P30DK079310, K23DK125718)
- National Center for Advancing Translational Sciences
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD100035)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Keywords
- COVID-19 pandemic, coarse-graining, single-cell data, disease outcome prediction, neutrophil response, Th17 cells, interferon-gamma, granzyme B, CD16, single-cell RNA sequencing, single-cell ATAC-seq, flow cytometry, high-dimensional data visualization
- MeSH terms
- Exome Sequencing, COVID-19, Chromatin, Humans, Transposases, Single-Cell Analysis
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
