Flow cytometry data analysis using Bioconductor.
1 : Ecole des Hautes Etudes en Santé Publique
(EHESP)
* : Corresponding author
UEB, Sorbonne-Paris-Cité
Avenue du Professeur-Léon-Bernard, CS 74312, 35043 Rennes Cedex -
France
Flow cytometry has benefited from the advent of high throughput techniques and now enables high content screening (FH-HCS), in both basic and clinical research, generating large complex data sets with many covariates. To model multiple covariates and interpret such FH-HCS experiment, rigorous workflow must be followed. Along with the task of acquiring the data come the tasks of storing, managing, assessing quality, analyzing, and summarizing to a condensed form that can be interpreted by researchers. Open source Bioconductor packages for analysis of flow cytometry data provide a unified framework for researchers to manage such workflow. Additionally they offer a research platform which bioinformaticians, computer scientists, and statisticians can use to develop novel methods. Currently, 12 Bioconductor packages are relevant to flow cytometry. In this presentation, I will first give an overview of their functionalities and then I will focus on the packages flowCore (infrastructure), flowViz (visualization), flowQ (quality assessment), and flowClust (clustering) to illustrate an analysis workflow of flow cytometry data using Bioconductor.
| Subject : | : | Oral Presentation |
| Date of production, writing | : | 2020-01-08 |
| Topics | : | From data analysis to mathematical modelling |
| Keywords | : | R ; Bioconductor ; FlowCytometry ; Analysis ; Visualization |
| PDF version | : |  PDF version |
