knitr::opts_chunk$set(echo = TRUE, cache = FALSE, eval = TRUE,
                      warning = TRUE, message = TRUE,
                      fig.width = 6, fig.height = 5)

Introduction

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.

See the detailed tutorial of CytoTree, please visit Tutorial of CytoTree.

Overview of 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 functionalities are integrated into one single channel which only requires a specified input data format. Computational functionalities of CytoTree can be divided into four main parts (Fig. 2.1): preprocessing, trajectory, analysis and visualization.