CytOpT
Optimal Transport for Gating Transfer in Cytometry Data with Domain Adaptation
Description
Supervised learning from a source distribution (with known segmentation into cell sub-populations) to fit a target distribution with unknown segmentation. It relies regularized optimal transport to directly estimate the different cell population proportions from a biological sample characterized with flow cytometry measurements. It is based on the regularized Wasserstein metric to compare cytometry measurements from different samples, thus accounting for possible mis-alignment of a given cell population across sample (due to technical variability from the technology of measurements). Supervised learning technique based on the Wasserstein metric that is used to estimate an optimal re-weighting of class proportions in a mixture model Details are presented in Freulon P, Bigot J and Hejblum BP (2023) <doi:10.1214/22-AOAS1660>.
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| r-devel-linux-x86_64-fedora-gcc | OK |
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| r-oldrel-macos-arm64 | OK |
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| r-oldrel-windows-x86_64 | OK |
| r-patched-linux-x86_64 | OK |
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| r-release-windows-x86_64 | OK |