LAUSR

Background The rapidly increasing dimensionality and throughput of flow and mass cytometry data necessitate new bioinformatics tools for analysis and interpretation, and the recently emerging single-cell-based algorithms provide a powerful strategy to meet this challenge. Results Here, we present CytoTree, an R/Bioconductor package designed to analyze and interpret multidimensional flow and mass cytometry data. CytoTree provides multiple computational functionalities that integrate most of the commonly used techniques in unsupervised clustering and dimensionality reduction and, more importantly, support the construction of a tree-shaped trajectory based on the minimum spanning tree algorithm. A graph-based algorithm is also implemented to estimate the pseudotime and infer intermediate-state cells. We apply CytoTree to several examples of mass cytometry and time-course flow cytometry data on heterogeneity-based cytology and differentiation/reprogramming experiments to illustrate the practical utility achieved in a fast and convenient manner. Conclusions CytoTree represents a versatile tool for analyzing multidimensional flow and mass cytometry data and to producing heuristic results for trajectory construction and pseudotime estimation in an integrated workflow.