These quantitative measurements allow to analyse and classify individual cells, facilitating diverse applications. Our main innovation is the methodology itself and the optimal transport techniques that we apply to flow cytometry analysis.įlow cytometry (FC) works with ‘high-dimensional quantitative measurement of light scatter and fluorescence emission properties of hundreds of thousands of individual cells in each analysed sample’ (see ). Our methodology provides a robust automated gating workflow that handles the intrinsic variability of flow cytometry data well. OptimalFlowTemplates + optimalFlowClassification addresses the problem of using supervised learning while accounting for biological and technical variability. Our code is freely available as optimalFlow, a Bioconductor R package at. We show that this procedure can outperform state of the art techniques in the proposed datasets. We also present optimalFlowClassification, which uses a database of gated cytometries and optimalFlowTemplates to assign cell types to a new cytometry. We show that supervised learning, restricted to the new groups, performs better than the same techniques applied to the whole collection. We propose optimalFlowTemplates, based on a similarity distance and Wasserstein barycenters, which clusters cytometries and produces prototype cytometries for the different groups. The present work is conceived as a combination of strategies to facilitate the task of supervised gating. This mixture of sources of variability makes the use of supervised machine learning for identification of cell populations difficult. 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. Biological variability is a well-known phenomenon produced by measurements on different individuals, with different characteristics such as illness, age, sex, etc. Data obtained from flow cytometry present pronounced variability due to biological and technical reasons.
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