CytOpT: Optimal Transport with Domain Adaptation for Interpreting Flow Cytometry data

06/16/2020
by   Paul Freulon, et al.
0

The automated analysis of flow cytometry measurements is an active research field. We introduce a new algorithm, referred to as CytOpT, using regularized optimal transport to directly estimate the different cell population proportions from a biological sample characterized with flow cytometry measurements. We rely 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). In this work, we rely on a supervised learning technique based on the Wasserstein metric that is used to estimate an optimal re-weighting of class proportions in a mixture model from a source distribution (with known segmentation into cell sub-populations) to fit a target distribution with unknown segmentation. Due to the high-dimensionality of flow cytometry data, we use stochastic algorithms to approximate the regularized Wasserstein metric to solve the optimization problem involved in the estimation of optimal weights representing the cell population proportions in the target distribution. Several flow cytometry data sets are used to illustrate the performances of CytOpT that are also compared to those of existing algorithms for automatic gating based on supervised learning.

READ FULL TEXT

page 10

page 13

research
07/18/2019

optimalFlow: Optimal-transport approach to flow cytometry gating and population matching

Data used in Flow Cytometry present pronounced variability due to biolog...
research
09/30/2022

Neural Unbalanced Optimal Transport via Cycle-Consistent Semi-Couplings

Comparing unpaired samples of a distribution or population taken at diff...
research
10/13/2022

On the potential benefits of entropic regularization for smoothing Wasserstein estimators

This paper is focused on the study of entropic regularization in optimal...
research
05/30/2022

Unbalanced CO-Optimal Transport

Optimal transport (OT) compares probability distributions by computing a...
research
04/24/2018

Data-driven regularization of Wasserstein barycenters with an application to multivariate density registration

We present a framework to simultaneously align and smooth data in the fo...
research
03/10/2023

Neural Gromov-Wasserstein Optimal Transport

We present a scalable neural method to solve the Gromov-Wasserstein (GW)...
research
03/17/2022

Low-rank Wasserstein polynomial chaos expansions in the framework of optimal transport

A unsupervised learning approach for the computation of an explicit func...

Please sign up or login with your details

Forgot password? Click here to reset