Uncovering axes of variation among single-cell cancer specimens
Open accessPublished version of a preprint
Abstract
While several tools have been developed to map axes of variation among individual cells, no analogous approaches exist for identifying axes of variation among multicellular biospecimens profiled at single-cell resolution. For this purpose, we developed 'phenotypic earth mover's distance' (PhEMD). PhEMD is a general method for embedding a 'manifold of manifolds', in which each datapoint in the higher-level manifold (of biospecimens) represents a collection of points that span a lower-level manifold (of cells). We apply PhEMD to a newly generated drug-screen dataset and demonstrate that PhEMD uncovers axes of cell subpopulational variation among a large set of perturbation conditions. Moreover, we show that PhEMD can be used to infer the phenotypes of biospecimens not directly profiled. Applied to clinical datasets, PhEMD generates a map of the patient-state space that highlights sources of patient-to-patient variation. PhEMD is scalable, compatible with leading batch-effect correction techniques and generalizable to multiple experimental designs.
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Several distance measures for scRNA-seq have been explored, including Wasserstein distance, maximum mean discrepancy, neighborhood-based measures, and energy distance.
“Multiple distance measures for scRNA-seq have been explored by the single-cell community in recent years, including Wasserstein distance computed in an optimal transport framework22,23, maximum mean discrepancy24, neighborhood-based measures17,18, and energy distance (E-distance)20.”
From scPerturb: harmonized single-cell perturbation data (2024) · cites this paper as [22]
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Cites
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174 · see citing works
The basics of epithelial-mesenchymal transition
Kalluri, R., & Weinberg, R. A. (2009) · Journal of Clinical Investigation · doi:10.1172/jci39104 · Cited by 10,196 · see citing works
The Epithelial-Mesenchymal Transition Generates Cells with Properties of Stem Cells
Mani, S. A., Guo, W., Liao, M.-J., Eaton, E. N., Ayyanan, A., Zhou, A. Y., Brooks, M. W., Reinhard, F., Zhang, C. C., Shipitsin, M. V., et al. (2008) · Cell · doi:10.1016/j.cell.2008.03.027 · Cited by 8,679 · see citing works
The Earth Mover's Distance as a Metric for Image Retrieval
Rubner, Y., Tomasi, C., & Guibas, L. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026543900054 · Cited by 5,547 · see citing works
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 · see citing works
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944 · see citing works
Nivolumab plus Ipilimumab versus Sunitinib in Advanced Renal-Cell Carcinoma
Motzer, R. J., Tannir, N. M., McDermott, D. F., Arén Frontera, O., Melichar, B., Choueiri, T. K., Plimack, E. R., Barthélémy, P., Porta, C., George, S., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1712126 · Cited by 4,656 · see citing works
Nieto, M. A., Huang, R. Y.-J., Jackson, R. A., & Thiery, J. P. (2016) · Cell · doi:10.1016/j.cell.2016.06.028 · Cited by 4,492 · see citing works
Circulating Breast Tumor Cells Exhibit Dynamic Changes in Epithelial and Mesenchymal Composition
Yu, M., Bardia, A., Wittner, B. S., Stott, S. L., Smas, M. E., Ting, D. T., Isakoff, S. J., Ciciliano, J. C., Wells, M. N., Shah, A. M., et al. (2013) · Science · doi:10.1126/science.1228522 · Cited by 2,476 · 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
Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis
Levine, J. H., Simonds, E. F., Bendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Litvin, O., Fienberg, H. G., Jager, A., Zunder, E. R., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.047 · Cited by 2,303 · 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-citationCausal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data
Sachs, K., Perez, O. D., Pe’er, D., Lauffenburger, D. A., & Nolan, G. P. (2005) · Science · doi:10.1126/science.1105809 · Cited by 1,724 · see citing works
Diffusion pseudotime robustly reconstructs lineage branching
Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F., & Theis, F. J. (2016) · Nature Methods · doi:10.1038/nmeth.3971 · Cited by 1,385 · see citing works
Spectral grouping using the nystrom method
Fowlkes, C. C., Belongie, S., Chung, F., & Malik, J. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1262185 · Cited by 1,310 · see citing works
Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses
Lavin, Y., Kobayashi, S., Leader, A., Amir, E.-A. D., Elefant, N., Bigenwald, C., Remark, R., Sweeney, R., Becker, C. D., Levine, J. H., et al. (2017) · Cell · doi:10.1016/j.cell.2017.04.014 · Cited by 1,209 · 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-citationAn Immune Atlas of Clear Cell Renal Cell Carcinoma
Chevrier, S., Levine, J., Zanotelli, V. R., Siliņa, K., Schulz, D., Bacac, M., Ries, C. H., Ailles, L., Jewett, M. A. S., Moch, H., et al. (2017) · Cell · doi:10.1016/j.cell.2017.04.016 · Cited by 1,009 · see citing works
Self-citationDiffusion maps, spectral clustering and reaction coordinates of dynamical systems
Nadler, B., Lafon, S., Coifman, R. R., & Kevrekidis, I. G. (2006) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2005.07.004 · Cited by 989 · see citing works
Bendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Simonds, E. F., Chen, T. J., Shenfeld, D. K., Nolan, G. P., & Pe’er, D. (2014) · Cell · doi:10.1016/j.cell.2014.04.005 · Cited by 906 · 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
Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering
Bengio, Y., Paiement, J.-F., Vincent, P., et al. (2003) · Cited by 864 · see citing works
The target landscape of clinical kinase drugs
Klaeger, S., Heinzlmeir, S., Wilhelm, M., Polzer, H., Vick, B., Koenig, P.-A., Reinecke, M., Ruprecht, B., Petzoldt, S., Meng, C., et al. (2017) · Science · doi:10.1126/science.aan4368 · Cited by 854 · see citing works
Novel functions of vimentin in cell adhesion, migration, and signaling
Ivaska, J., Pallari, H., Nevo, J., & Eriksson, J. (2007) · Experimental Cell Research · doi:10.1016/j.yexcr.2007.03.040 · Cited by 786 · see citing works
Normalization of mass cytometry data with bead standards
Finck, R., Simonds, E. F., Jager, A., Krishnaswamy, S., Sachs, K., Fantl, W. J., Pe’er, D., Nolan, G. P., & Bendall, S. C. (2013) · Cytometry Part A · doi:10.1002/cyto.a.22271 · Cited by 737 · see citing works
Self-citationImplications of the Hybrid Epithelial/Mesenchymal Phenotype in Metastasis
Jolly, M. K. (2015) · Frontiers in Oncology · doi:10.3389/fonc.2015.00155 · Cited by 684 · see citing works
<i>destiny</i> : diffusion maps for large-scale single-cell data in R
Angerer, P., Haghverdi, L., Büttner, M., Theis, F. J., Marr, C., & Buettner, F. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btv715 · Cited by 640 · see citing works
Ricardo, S. A. V., Vieira, A. F., Gerhard, R., Leitão, D., Pinto, R., Cameselle‐Teijeiro, J., Milanezi, F., Schmitt, F. C., & Paredes, J. (2011) · Journal of Clinical Pathology · doi:10.1136/jcp.2011.090456 · Cited by 621 · see citing works
A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens
Gasperini, M., Hill, A. J., McFaline-Figueroa, J. L., Martin, B., Kim, S., Zhang, M. D., Jackson, D., Leith, A., Schreiber, J., Noble, W. S., et al. (2019) · Cell · doi:10.1016/j.cell.2018.11.029 · Cited by 613 · see citing works
Zunder, E. R., Finck, R., Behbehani, G. K., Amir, E.-A. D., Krishnaswamy, S., Gonzalez, V. D., Lorang, C. G., Bjornson, Z., Spitzer, M. H., Bodenmiller, B., et al. (2015) · Nature Protocols · doi:10.1038/nprot.2015.020 · Cited by 596 · see citing works
Self-citationWishbone identifies bifurcating developmental trajectories from single-cell data
Setty, M., Tadmor, M. D., Reich-Zeliger, S., Angel, O., Salame, T. M., Kathail, P., Choi, K., Bendall, S., Friedman, N., & Pe'er, D. (2016) · Nature Biotechnology · doi:10.1038/nbt.3569 · Cited by 558 · see citing works
Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators
Bodenmiller, B., Zunder, E. R., Finck, R., Chen, T. J., Savig, E. S., Bruggner, R. V., Simonds, E. F., Bendall, S. C., Sachs, K., Krutzik, P. O., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2317 · Cited by 546 · see citing works
Self-citationHammers, H. J., Plimack, E. R., Infante, J. R., Rini, B. I., McDermott, D. F., Lewis, L. D., Voss, M. H., Sharma, P., Pal, S. K., Razak, A. R. A., et al. (2017) · Journal of Clinical Oncology · doi:10.1200/jco.2016.72.1985 · Cited by 504 · see citing works
Li, W., Ma, H., Zhang, J., Zhu, L., Wang, C., & Yang, Y. (2017) · Scientific Reports · doi:10.1038/s41598-017-14364-2 · Cited by 455 · see citing works
Zhu, H., Mitsuhashi, N., Klein, A., Barsky, L. W., Weinberg, K. I., Barr, M. L., Demetriou, A. A., & Wu, G. D. (2005) · Stem Cells · doi:10.1634/stemcells.2005-0186 · Cited by 408 · see citing works
PD-1 Blockade Expands Intratumoral Memory T Cells
Ribas, A., Shin, D. S., Zaretsky, J., Frederiksen, J., Cornish, A., Avramis, E., Seja, E., Kivork, C., Siebert, J., Kaplan-Lefko, P., et al. (2016) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-15-0210 · Cited by 405 · cited 15× · see citing works
“1): (1) Compute a distance between each pair of datasets (that is, experimental conditions) as follows: (a) Embed points within each dataset using PHATE 8 (b) Cluster datapoints using spectral clustering (c) Represent each dataset as a vector of relative cluster proportions (d) Compute the distance between two dataset…”
BackgroundInfluentialHighly multiparametric analysis by mass cytometry
Ornatsky, O., Bandura, D. R., Baranov, V. I., Nitz, M., Winnik, M. A., & Tanner, S. D. (2010) · Journal of Immunological Methods · doi:10.1016/j.jim.2010.07.002 · Cited by 368 · see citing works
Elkabets, M., Vora, S., Juric, D., Morse, N., Mino-Kenudson, M., Muranen, T., Tao, J., Campos, A. B., Rodon, J., Ibrahim, Y. H., et al. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3005747 · Cited by 332 · see citing works
Ramos, T. L., Sánchez‐Abarca, L. I., Muntión, S., Preciado, S., Puig, N., López-Ruano, G., Hernández‐Hernández, Á., Redondo, A., Ortega, R., Rodríguez, C., et al. (2016) · Cell Communication and Signaling · doi:10.1186/s12964-015-0124-8 · Cited by 313 · see citing works
A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry
Damond, N., Engler, S., Zanotelli, V., Schapiro, D., Wasserfall, C., Kusmartseva, I., Nick, H., Thorel, F., Herrera, P., Atkinson, M., et al. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2018.11.014 · Cited by 288 · 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-citationLiu, L. L., Landskron, J., Ask, E. H., Enqvist, M., Sohlberg, E., Traherne, J. A., Hammer, Q., Goodridge, J. P., Larsson, S., Jayaraman, J., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.04.005 · Cited by 222 · see citing works
Ma, F., Li, H., Wang, H., et al. (2014) · Cancer Letters · doi:10.1016/j.canlet.2014.06.022 · Cited by 151 · 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-citationDifferential Earth Mover's Distance with Its Applications to Visual Tracking
Zhao, Q., Yang, Z. Q., & Hai, T. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.299 · Cited by 125 · see citing works
Orlova, D. Y., Zimmerman, N., Meehan, S., Meehan, C., Waters, J., Ghosn, E., Filatenkov, A., Kolyagin, G. A., Gernez, Y., Tsuda, S., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0151859 · Cited by 114 · see citing works
Effect of diet on the queuosine family of tRNAs of germ-free mice.
Farkas, W. R. (1980) · Journal of Biological Chemistry · doi:10.1016/s0021-9258(18)43648-4 · Cited by 111 · see citing works
Behbehani, G. K., Samusik, N., Bjornson, Z. B., Fantl, W. J., Medeiros, B. C., & Nolan, G. P. (2015) · Cancer Discovery · doi:10.1158/2159-8290.cd-15-0298 · Cited by 106 · see citing works
Hemming, V. G., Prince, G. A., Horswood, R. L., et al. (1985) · The Journal of Infectious Diseases · doi:10.1093/infdis/152.5.1083 · Cited by 100 · see citing works
Alignment of single-cell trajectories to compare cellular expression dynamics
Alpert, A., Moore, L. S., Dubovik, T., & Shen-Orr, S. S. (2018) · Nature Methods · doi:10.1038/nmeth.4628 · Cited by 96 · see citing works
Py2T Murine Breast Cancer Cells, a Versatile Model of TGFβ-Induced EMT In Vitro and In Vivo
Waldmeier, L., Meyer‐Schaller, N., Diepenbruck, M., & Christofori, G. (2012) · PLoS ONE · doi:10.1371/journal.pone.0048651 · Cited by 67 · see citing works
Selective Aldosterone Deficiency1
Vagnucci, A. H. (1969) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem-29-2-279 · Cited by 65 · see citing works
Supervised Earth Mover’s Distance Learning and Its Computer Vision Applications
Wang, F., & Guibas, L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33718-5_32 · Cited by 47 · see citing works
Reproductive hormones and aggressive behavior.
Barfield, R. J. (1984) · PubMed · Cited by 47 · see citing works
Soft tissue sarcomas: Current trends in diagnosis and management
Antman, K. H., Eilber, F. R., & Shiu, M. H. (1989) · Current Problems in Cancer · doi:10.1016/0147-0272(89)90015-9 · Cited by 45 · see citing works
Transportation distances and human perception of melodic similarity
Typke, R., Wiering, F., & Veltkamp, R. C. (2007) · Musicae Scientiae · doi:10.1177/102986490701100107 · Cited by 40 · see citing works
Quantitative assessment of cell population diversity in single-cell landscapes
Liu, Q., Herring, C. A., Sheng, Q., Ping, J., Simmons, A. J., Chen, B., Banerjee, A., Li, W., Gu, G., Coffey, R. J., et al. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2006687 · Cited by 39 · see citing works
Learning Wasserstein EmbeddingsPreprint
Courty, N., Flamary, R., & Ducoffe, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.07457 · Cited by 36 · see citing works
A Practical Guide to Multiplexed Mass Cytometry
Zivanovic, N., Jacobs, A., & Bodenmiller, B. (2013) · Current topics in microbiology and immunology · doi:10.1007/82_2013_335 · Cited by 34 · see citing works
Self-citationComparative potentiation of malathion by triorthotolyl phosphate in four classes of vertebrates
Cohen, S. D., & Murphy, S. D. (1970) · Toxicology and Applied Pharmacology · doi:10.1016/0041-008x(70)90075-x · Cited by 24 · see citing works
Evidence for the presence of tetrodotoxin in a powder used in Haiti for zombification
Benedek, C., & Rivier, L. (1989) · Toxicon · doi:10.1016/0041-0101(89)90210-9 · Cited by 21 · see citing works
Stringency in the absence of ppGpp accumulation in Rhodobacter sphaeroides
Acosta, R., & Lueking, D. R. (1987) · Journal of Bacteriology · doi:10.1128/jb.169.2.908-912.1987 · Cited by 16 · see citing works
Further studies on knee ligament failure in broiler breeding fowls
Duff, S. R. I. (1986) · Journal of Comparative Pathology · doi:10.1016/0021-9975(86)90069-1 · Cited by 14 · see citing works
Approximately-isometric diffusion maps
Salhov, M., Bermanis, A., Wolf, G., et al. (2014) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2014.05.002 · Cited by 13 · see citing works
Self-citationider: Intrinsic Dimension Estimation with R
Hino, H. (2017) · The R Journal · doi:10.32614/rj-2017-054 · Cited by 12 · see citing works
INCIDENCE OF GLAUCOMA IN OPTOMETRIC PRACTICE — AN EIGHT YEAR EVALUATION OF 6,580 TONOGRAMS
Fair, R. G. (1972) · Optometry and Vision Science · doi:10.1097/00006324-197209000-00008 · Cited by 7 · see citing works
Quantitative assessment of cell population diversity in single-cell landscapesPreprint
Liu, Q., Herring, C. A., Sheng, Q., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/333393 · Cited by 6 · see citing works
[An imposing start--a comprehensive outline].
Höfling, E. (1972) · PubMed · Cited by 5 · see citing works
[Injuries and damage to the locomotor system in diving].
Strupler, M., & Saxer, U. (1982) · PubMed · Cited by 2 · see citing works
Forty-third All-India Medical Conference Jabalpur: December 1967: Inaugural address.
Jn, M. (1968) · PubMed · Cited by 2 · see citing works
[Effect of different alpha-tocopherol intakes on vitamin A and its utilization by rats].
Gronowska-Senger, A., Raczyńska, M., & Berger, S. T. A. (1973) · PubMed · Cited by 1 · see citing works
Winkler, G., & Bahner, F. (1967) · PubMed · Cited by 1 · see citing works
[Calcification of the internal iliac artery].
(1974) · PubMed · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- [32]Williams CKI & Seeger M in Advances in Neural Information Processing Systems Vol. 13 (eds Leen TK) 682– 688 (MIT Press, 2001).
- [45]Wang F & Guibas L in Computer Vision—ECCV 2012 Vol. 7572 (eds Fitzgibbon A et al.) 442– 455 (Springer, 2012).
- [52]Zivanovic N Jacobs A & Bodenmiller B in High-Dimensional Single Cell Analysis Vol. 377 (eds Fienberg HG & Nolan GP) 95– 109 (Springer, 2013).
- [55]Levina E & Bickel PJ in Advances in Neural Information Processing Systems Vol. 17 (eds Saul LK) 777– 784 (MIT Press, 2005).
Cited by
Sorted by citations
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Peidli, S., Green, T. D., Shen, C., Gross, T., Min, J., Garda, S., Yuan, B., Schumacher, L. J., Taylor-King, J. P., Marks, D. S., et al. (2024) · Nature Methods · doi:10.1038/s41592-023-02144-y · Cited by 194 · see citing works
Machine learning for perturbational single-cell omics
Ji, Y., Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.016 · Cited by 137 · see citing works
Multiscale PHATE identifies multimodal signatures of COVID-19
Kuchroo, M., Huang, J., Wong, P., Grenier, J.-C., Shung, D., Tong, A., Lucas, C., Klein, J., Burkhardt, D. B., Gigante, S., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01186-x · Cited by 72 · see citing works
Self-citationTrellis tree-based analysis reveals stromal regulation of patient-derived organoid drug responses
Ramos Zapatero, M., Tong, A., Opzoomer, J. W., O’Sullivan, R., Cardoso Rodriguez, F., Sufi, J., Vlckova, P., Nattress, C., Qin, X., Claus, J., et al. (2023) · Cell · doi:10.1016/j.cell.2023.11.005 · Cited by 72 · see citing works
Self-citation
- Authors
- Chen, W. S., Zivanovic, N., van Dijk, D., Wolf, G., Bodenmiller, B., & Krishnaswamy, S.
- Affiliations
- Yale University (US)
- University of Zurich (CH)
- Université de Montréal (CA)
- Corresponding author
- Bodenmiller, Bernd
- Krishnaswamy, Smita
- Venue
- Nature Methods
- Published
- 2020-01-13
- Volume
- 17
- Issue
- 3
- Pages
- 302-310
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC7339867
- OpenAlex
- W2998995887
- Cited by
- 48
- References
- 73
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 11 · 2023: 10 · 2022: 7 · 2021: 9 · 2020: 4
- FWCI
- 2.43
- Type
- journal article
- Language
- en
- Open access
- green
- www.zora.uzh.ch
- Licence
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- PubMed Central (acceptedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- U.S. Department of Health and Human Services (R01GM135929, T35DK104689)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (144874)
- National Institutes of Health (UL1TR001863)
- National Institute of General Medical Sciences (R01GM130847, R01GM135929)
- National Institute of Diabetes and Digestive and Kidney Diseases (UC4DK108132, T35DK104689)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Keywords
- drug screening, patient-to-patient variation, batch effect correction, cancer phenotypes
- MeSH terms
- Algorithms, Animals, Antineoplastic Agents, Biopsy, Breast Neoplasms, Cytophotometry, Drug Screening Assays, Antitumor, Enzyme Inhibitors, Female, Humans, Image Interpretation, Computer-Assisted, Neoplasm Metastasis, Pattern Recognition, Automated, Phenotype, Recombinant Proteins, Software, Mammary Neoplasms, Animal, Cluster Analysis, Transforming Growth Factor beta, Mice, Epithelial-Mesenchymal Transition, Single-Cell Analysis
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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 ›
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