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Cell Spotter (CSPOT): A machine-learning approach to automated cell spotting and quantification of highly multiplexed tissue images
View ORCID ProfileAjit J. Nirmal, View ORCID ProfileClarence Yapp, View ORCID ProfileSandro Santagata, View ORCID ProfilePeter K. Sorger
doi: https://doi.org/10.1101/2023.11.15.567196
Ajit J. Nirmal
1Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
2Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
3Department of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
Clarence Yapp
1Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
2Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
Sandro Santagata
1Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
2Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
4Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
Peter K. Sorger
1Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
2Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
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Posted November 17, 2023.
Cell Spotter (CSPOT): A machine-learning approach to automated cell spotting and quantification of highly multiplexed tissue images
Ajit J. Nirmal, Clarence Yapp, Sandro Santagata, Peter K. Sorger
bioRxiv 2023.11.15.567196; doi: https://doi.org/10.1101/2023.11.15.567196
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