OptimalFlow: optimal transport approach to flow cytometry gating and population matching.

ABSTRACT:

Background

Data obtained from flow cytometry present pronounced variability due to biological and technical reasons. Biological variability is a well-known phenomenon produced by measurements on different individuals, with different characteristics such as illness, age, sex, etc. The use of different settings for measurement, the variation of the conditions during experiments and the different types of flow cytometers are some of the technical causes of variability. This mixture of sources of variability makes the use of supervised machine learning for identification of cell populations difficult. The present work is conceived as a combination of strategies to facilitate the task of supervised gating.

Results

We propose optimalFlowTemplates, based on a similarity distance an

SUBMITTER: Del Barrio E 

PROVIDER: S-EPMC7590740 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature