Computational Tools for Cytometry Data Analysis

Identification: Todorov, Helena


Computational Tools for Cytometry Data Analysis

Helena Todorov3, Fabien Crauste1,2,5, Olivier Gandrillon1,4, Emilie Westeel3, Christophe Arpin3, Jacqueline Marvel3

1Team Dracula, Inria, 69603 Villeurbanne, France; 2Institut Camille Jordan, Université de Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, 43 Boulevard du 11 novembre 1918, 69622 Villeurbanne Cedex, France; 3CIRI, ICL, INSERM U1111, Université Claude Bernard Lyon 1, CNRS UMR 5308, École Normale Supérieure de Lyon, Université de Lyon, 69007 Lyon, France; 4Laboratory of Biology and Modelling of the Cell, Université de Lyon, ENS de Lyon, Université Claude Bernard, CNRS UMR 5239, INSERM U1210, 46 allée d'Italie Site Jacques Monod, 69007 Lyon, France

Recent advances in biotechnologies have drastically increased the possibilities in single cell analysis. Nowadays, cells can be tagged with up to a hundred of markers, providing a tremendous quantity of data to analyze. In order to fully understand the developmental processes followed by cells in reaction to external aggressions, new tools should be used. These tools, by their ability to take into account the structure of cells in as much dimensions as the markers they have been tagged with, represent a great assistance in cytometry data analyses. In this poster, we combined different bioinformatic tools for cytometry data visualization and lineage recovery to analyze the response of T CD8 lymphocytes to a viral infection. Here, we compare the results of our semi-supervised approach to the results that were manually obtained on the same dataset. The combination of a manual and computational approach generated a new biological hypothesis, that could explain the developmental trajectory of T CD8 lymphocytes in response to a viral infection.


Credits: None available.

You must be logged in and own this product in order to post comments.