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 (2021) <arXiv:2006.09003>.
Package details |
|
---|---|
Author | Boris Hejblum [aut, cre], Paul Freulon [aut], Kalidou Ba [aut, trl] |
Maintainer | Boris Hejblum <boris.hejblum@u-bordeaux.fr> |
License | GPL (>= 2) |
Version | 0.9.4 |
URL | https://sistm.github.io/CytOpT-R/ https://github.com/sistm/CytOpT-R/ |
Package repository | View on CRAN |
Installation |
Install the latest version of this package by entering the following in R:
|
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.