Cytofast: A workflow for visual and quantitative analysis of flow and mass cytometry data to discover immune signatures and correlations

Abstract: Multi-parametric flow and mass cytometry allows exceptional high-resolution exploration of the cellular composition of the immune system. A large panel of computational tools have been developed to analyze the high-dimensional landscape of the data generated. Analysis frameworks such as FlowSOM or Cytosplore incorporate clustering and dimensionality reduction techniques and include algorithms allowing visualization of multi-parametric cytometric analysis. To additionally provide means to quantify specific cell clusters and correlations between samples, we developed an R-package, called cytofast, for further downstream analysis. Specifically, cytofast enables the visualization and quantification of cell clusters for an efficient discovery of cell populations associated with diseases or physiology. We used cytofast on mass and flow cytometry datasets based on the modulation of the immune system upon immunotherapy. With cytofast, we rapidly generated visual representations of group-related immune cell clusters and showed correlations with the immune system composition. We discovered macrophage subsets that significantly decrease upon cancer immunotherapy and distinct prime-boost effects of prophylactic vaccines on the myeloid compartment. Cytofast is a time-efficient tool for comprehensive cytometric analysis to reveal immune signatures and correlations.

Guillaume Beyrend, Koen Stam, Thomas Höllt, Ferry Ossendorp, and Ramon Arens. Cytofast: A workflow for visual and quantitative analysis of flow and mass cytometry data to discover immune signatures and correlations. Computational and Structural Biotechnology Journal, 2018.
@article { bib:2018_cytofast,
author = { Guillaume Beyrend and Koen Stam and Thomas H{\"o}llt and Ferry Ossendorp and Ramon Arens },
title = { Cytofast: A workflow for visual and quantitative analysis of flow and mass cytometry data to discover immune signatures and correlations },
journal = { Computational and Structural Biotechnology Journal },
year = { 2018 },
doi = { 10.1016/j.csbj.2018.10.004 },
}