CytOpT
0.9.8Optimal Transport for Gating Transfer in Cytometry Data with Domain Adaptation
Overview
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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- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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4 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/sistm/cytopt-r on 2026-07-19.
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5 releases. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 0.9.8Latest
- unarchivedReturned to CRAN2025-04-01
- archivedRemoved from CRAN2025-03-31repeated policy violation
- 0.9.72025-03-30 · diff ↗
- unarchivedReturned to CRAN2025-03-30
- archivedRemoved from CRAN2025-03-28policy violation Averaging ca 4 cores, even after update
- 0.9.62025-03-26 · diff ↗
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- 0.9.42022-02-09 · diff ↗
- 0.9.22022-02-07
- RR 4.1.0 released · 2021-05-18
Package metadata
- First published
- 2022-02-07
- Total releases
- 5 / 4 yrs
- License
- GPL (>= 2) OSI
- Download size
- 2.0 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet