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Master Thesis

Développement d'un pipeline automatisé d'analyse de données de cytométrie de masse pour l'identification de populations immunitaires

Abstract : Until recently, flow cytometry has been the most widely used method to study immune cells phenotype. Mass cytometry is a new promising technology which enables the measurement of over forty markers per cell simultaneously with a higher resolution. However, the increase in the number of features entails the need of new analysis methods. Indeed, manual analysis is very fastidious, time-consuming and analyst-dependant. In this article, we present a pipeline for automated, fast and objective mass cytometry data analysis. It is divided into four parts: raw data pre-processing, quality control, identification of immune subpopulations, and their visualizations. Two different approaches for cell-type identification are available. They can be used alone or in parallel to compare the results. The first one is a supervised method based on the OpenCyto framework. It consists in the automation of manual gating usually conducted for flow cytometry data. The second one uses FlowSOM and Consensus Clustering, two unsupervised embedding algorithms, in order to cluster cell into different populations. Afterward, identified populations can be displayed through the dimension reduction algorithm UMAP. In the near future, a graphical user interface will be developed with R Shiny in order to facilitate the access and the implementation of the pipeline.
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Master Thesis
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https://dumas.ccsd.cnrs.fr/dumas-02369998
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Submitted on : Tuesday, November 19, 2019 - 11:33:33 AM
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Juliette Maes. Développement d'un pipeline automatisé d'analyse de données de cytométrie de masse pour l'identification de populations immunitaires. Sciences du Vivant [q-bio]. 2019. ⟨dumas-02369998⟩

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