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. Cytofast is available at Bioconductor.
Highlights
Mass cytometry can detect as many as 40 markers present on millions of single cells
The number of studies based on mass cytometry has considerably increased over the last few years, broadening simultaneously the choice of clustering techniques, such as SPADE [1], FlowMaps [2], FlowSOM [3], Phenograph [4], VorteX [5] and Scaffold maps [6], and dimensionality reductionbased techniques including PCA [7], t-SNE [8] and Diffusion Map [9]
We focused on the clustering analysis with Cytosplore using mass and flow cytometry datasets
Summary
Mass cytometry (cytometry by time-of-flight; CyTOF) can detect as many as 40 markers present on millions of single cells. A recent new computational tool, HSNE [10], embedded in Cytosplore [11], proposed a combination of the two afore-mentioned techniques, building a hierarchical representation of the complete data that preserves the non-linear high-dimensional relationships between cells and avoids any down-sampling [12]. The aforementioned computational tools lack, the means for downstream analysis, where no automatic process has been proposed to link clusters abundance with e.g. clinical outcome and to visually interpret the data in the context of the experimental setup. Citrus [13] is currently a broadly used analysis tool for statistical comparison but presents several limitations. Citrus is using agglomerative clustering, which brings complexity in data visualization
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