The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Automated mapping of phenotype space with single-cell data
Open accessAbstract
Accurate identification of cell subsets in complex populations is key to discovering novelty in multidimensional single-cell experiments. We present X-shift (http://web.stanford.edu/~samusik/vortex/), an algorithm that processes data sets using fast k-nearest-neighbor estimation of cell event density and arranges populations by marker-based classification. X-shift enables automated cell-subset clustering and access to biological insights that 'prior knowledge' might prevent the researcher from discovering.
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Cites
Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E.-A. D., Krutzik, P. O., Finck, R., Bruggner, R. V., Melamed, R., Trejo, A., Ornatsky, O. I., et al. (2011) · Science · doi:10.1126/science.1198704 · Cited by 2,279
Self-citationExtracting a cellular hierarchy from high-dimensional cytometry data with SPADE
Qiu, P., Simonds, E. F., Bendall, S. C., Gibbs, K. D., Bruggner, R. V., Linderman, M. D., Sachs, K., Nolan, G. P., & Plevritis, S. K. (2011) · Nature Biotechnology · doi:10.1038/nbt.1991 · Cited by 956
Self-citationBendall, 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
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Cited by
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
Deep profiling of mouse splenic architecture with CODEX multiplexed imaging
Goltsev, Y., Samusik, N., Kennedy-Darling, J., Bhate, S., Hale, M., Vazquez, G., Black, S., & Nolan, G. P. (2018) · Cell · doi:10.1016/j.cell.2018.07.010 · Cited by 1,501
“Moreover, application of X-shift phenotype-mapping algorithm (Samusik et al., 2016) tailored to parsing the multidimensional single-cell data enabled the detection of rare cells types (such as CD4 MHCII [Lti] cells, CD11c B cells [ABC cells]) and simultaneous placement of those cells in the tissue architecture.”
NBB-Psy, Böttcher, C., Schlickeiser, S., Sneeboer, M. A. M., Kunkel, D., Knop, A., Paza, E., Fidzinski, P., Kraus, L., Snijders, G. J. L., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0290-2 · Cited by 436
An ex vivo tumor fragment platform to dissect response to PD-1 blockade in cancer
Voabil, P., de Bruijn, M., Roelofsen, L. M., Hendriks, S. H., Brokamp, S., van den Braber, M., Broeks, A., Sanders, J., Herzig, P., Zippelius, A., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01398-3 · Cited by 380
Fundamental limits on dynamic inference from single-cell snapshots
Weinreb, C., Wolock, S., Tusi, B. K., Socolovsky, M., & Klein, A. M. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1714723115 · Cited by 341
Ajami, B., Samusik, N., Wieghofer, P., Ho, P. P., Crotti, A., Bjornson, Z., Prinz, M., Fantl, W. J., Nolan, G. P., & Steinman, L. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0100-x · Cited by 338
Self-citationSPRING: a kinetic interface for visualizing high dimensional single-cell expression data
Weinreb, C., Wolock, S., & Klein, A. M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx792 · Cited by 298
Testing for differential abundance in mass cytometry data
Lun, A. T. L., Richard, A. C., & Marioni, J. C. (2017) · Nature Methods · doi:10.1038/nmeth.4295 · Cited by 181
Impact of similarity metrics on single-cell RNA-seq data clustering
Kim, T., Chen, I. R., Lin, Y., Wang, A. Y.-Y., Yang, J. Y. H., & Yang, P. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bby076 · Cited by 138
Severe, N., Karabacak, N. M., Gustafsson, K., Baryawno, N., Courties, G., Kfoury, Y., Kokkaliaris, K. D., Rhee, C., Lee, D., Scadden, E. W., et al. (2019) · Cell Stem Cell · doi:10.1016/j.stem.2019.06.003 · Cited by 130
High-resolution myogenic lineage mapping by single-cell mass cytometry
Porpiglia, E., Samusik, N., Ho, A. T. V., Cosgrove, B. D., Mai, T., Davis, K. L., Jager, A., Nolan, G. P., Bendall, S. C., Fantl, W. J., et al. (2017) · Nature Cell Biology · doi:10.1038/ncb3507 · Cited by 128
Self-citationComprehensive Immune Monitoring of Clinical Trials to Advance Human Immunotherapy
Hartmann, F. J., Babdor, J., Gherardini, P. F., Amir, E.-A. D., Jones, K., Sahaf, B., Marquez, D. M., Krutzik, P., O’Donnell, E., Sigal, N., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.06.049 · Cited by 124
Self-citationZhang, W., Li, I., Reticker-Flynn, N. E., Good, Z., Chang, S., Samusik, N., Saumyaa, S., Li, Y., Zhou, X., Liang, R., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01498-z · Cited by 108
Self-citationKimmel, J. C., Chang, A. Y., Brack, A. S., & Marshall, W. F. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005927 · Cited by 54
“Methods have been developed to infer cell state lineages from observations at a single time point[18,19], but these methods are not able to quantify dynamics and assume that proximity in the measured variable space describes a transition relationship between states.”
BackgroundGasparri, A. M., Sacchi, A., Basso, V., Cortesi, F., Freschi, M., Rrapaj, E., Bellone, M., Casorati, G., Dellabona, P., Mondino, A., et al. (2019) · Small · doi:10.1002/smll.201903462 · Cited by 37
Fundamental limits on dynamic inference from single cell snapshots
Weinreb, C., Wolock, S., Tusi, B. K., Socolovsky, M., & Klein, A. M. (2017) · doi:10.1101/170118 · Cited by 27
Weber, E. W., Lynn, R. C., Parker, K. R., Anbunathan, H., Lattin, J., Sotillo, E., Good, Z., Malipatlolla, M., Xu, P., Vandris, P., et al. (2020) · doi:10.1101/2020.01.26.920496 · Cited by 18
Self-citation
Metadata
- Authors
- Samusik, N., Good, Z., Spitzer, M. H., Davis, K. L., & Nolan, G. P.
- Venue
- Nature Methods
- Published
- 2016-05-16
- Volume
- 13
- Issue
- 6
- Pages
- 493-496
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2358421111
- Cited by
- 419
- References
- 3
- FWCI
- 13.69
- Type
- journal article
- Language
- en
- Open access
- green
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