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A robust and interpretable end-to-end deep learning model for cytometry data
2020
Proceedings of the National Academy of Sciences of the United States of America
Cytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Existing approaches in interpreting and using cytometry measurements include manual or automated gating to identify cell subsets from the cytometry data, providing highly intuitive results but may lead to significant information loss, in that additional details in measured or correlated cell signals might be missed. In this study, we propose
doi:10.1073/pnas.2003026117
pmid:32801215
fatcat:obx4nw3bsnb3zm6y4ppwtut4ei