The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data
Open accessPublished version of a preprint
Abstract
Single-cell, spatially resolved omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source, computational histology topography cytometry analysis toolbox (histoCAT) to enable interactive, quantitative, and comprehensive exploration of individual cell phenotypes, cell–cell interactions, microenvironments, and morphological structures within intact tissues. We highlight the unique abilities of histoCAT through analysis of highly multiplexed mass cytometry images of human breast cancer tissues.
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Citations
Cites
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Spatially resolved, highly multiplexed RNA profiling in single cells
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Cited by
Data-efficient and weakly supervised computational pathology on whole-slide images
Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Chen, R. J., Barbieri, M., & Mahmood, F. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00682-w · Cited by 2,282
Eleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327
Deciphering cell–cell interactions and communication from gene expression
Armingol, E., Officer, A., Harismendy, O., & Lewis, N. E. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-020-00292-x · Cited by 1,267
Giotto: a toolbox for integrative analysis and visualization of spatial expression data
Dries, R., Zhu, Q., Dong, R., Eng, C.-H. L., Li, H., Liu, K., Fu, Y., Zhao, T., Sarkar, A., Bao, F., et al. (2021) · Genome Biology · doi:10.1186/s13059-021-02286-2 · Cited by 917
Pan-cancer integrative histology-genomic analysis via multimodal deep learning
Chen, R. J., Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Noor, Z., Shaban, M., Shady, M., Williams, M., Joo, B., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.07.004 · Cited by 660
Metadata
- Authors
- Schapiro, D., Jackson, H. W., Raghuraman, S., Fischer, J. R., Zanotelli, V. R. T., Schulz, D., Giesen, C., Catena, R., Varga, Z., & Bodenmiller, B.
- Venue
- Nature Methods
- Published
- 2017-08-07
- Volume
- 14
- Issue
- 9
- Pages
- 873-876
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2743344672
- Cited by
- 659
- References
- 25
- FWCI
- 11.55
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
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- Schapiro, D., Jackson, H. W., Raghuraman, S., Fischer, J. R., Zanotelli, V. R. T., Schulz, D., Giesen, C., Catena, R., Varga, Z., & Bodenmiller, B. (2017). histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nature Methods. https://doi.org/10.1038/nmeth.4391
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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