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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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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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