Although multidimensional single-cell-based flow and mass cytometry have been increasingly applied to microenvironmental composition and stem-cell research, integrated analysis workflows to facilitate the interpretation of experimental cytometry data remain underdeveloped. We present CytoTree, a comprehensive R package designed for the analysis and interpretation of flow and mass cytometry data. We applied CytoTree to mass cytometry and time-course flow cytometry data to demonstrate the usage and practical utility of its computational modules. CytoTree is a reliable tool for multidimensional cytometry data workflows and produces compelling results for trajectory construction and pseudotime estimation.
Overview of CytoTree workflow
The CytoTree package is developed to complete the majority of standard analysis and visualization workflow for FCS data. In CytoTree workflow, an S4 object in R is built to implement the statistical and computational approach, and all computational modules are integrated into one single channel which only requires a specified input data format.
CytoTree can help you to perform four main types of analysis:
CytoTreecan help you to discover and identify subtypes of cells.
Dimensionality Reduction. Several dimensionality reduction methods are provided in
CytoTreepackage such as Principal Components Analysis (PCA), t-distributed Stochastic Neighbor Embedding (tSNE), Diffusion Maps and Uniform Manifold Approximation and Projection (UMAP). CytoTree provides both cell-based and cluster-based dimensionality reduction.
CytoTreecan help you to construct the cellular differential based on minimum spanning tree (MST) algorithm.
Pseudotime and Intermediate states definition. The root cells need to be defined by users. The trajctroy value will be calculated based on Shortest Path from root cells and leaf cells using R
igraphpackage. Subset FCS data set in
CytoTreeand find the key intermediate cell states based on trajectory value.