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Data obtained from Flow Cytometry present pronounced variability due tobiological and technical reasons. Biological variability is a well-knownphenomenon produced by measurements on different individuals, with differentcharacteristics such as illness, age, sex, etc. The use of different settingsfor measurement, the variation of the conditions during experiments and thedifferent types of flow cytometers are some of the technical causes ofvariability. This mixture of sources of variability makes the use of supervisedmachine learning for identification of cell populations difficult. The presentwork is conceived as a combination of strategies to facilitate the task ofsupervised gating.  We propose $optimalFlowTemplates$, based on a similarity distance and$\text{Wasserstein barycenters}$, which clusters cytometries and producesprototype cytometries for the different groups. We show that supervisedlearning, restricted to the new groups, performs better than the sametechniques applied to the whole collection. We also present$optimalFlowClassification$, which uses a database of gated cytometries andoptimalFlowTemplates to assign cell types to a new cytometry. We show that thisprocedure can outperform state of the art techniques in the proposed datasets.Our code is freely available as $optimalFlow$ a Bioconductor R package athttps://bioconductor.org/packages/optimalFlow.  optimalFlowTemplates+optimalFlowClassification addresses the problem of usingsupervised learning while accounting for biological and technical variability.Our methodology provides a robust automated gating workflow that handles theintrinsic variability of flow cytometry data well. Our main innovation is themethodology itself and the optimal-transport techniques that we apply to flowcytometry analysis.

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