CytoTree

CytoTree performs trajectory inference, pseudotime estimation, and cellular subpopulation identification from high-dimensional flow and mass cytometry single-cell data.

Key Features:

• Cellular subpopulation identification: Facilitates detection and characterization of distinct cellular subpopulations within complex flow and mass cytometry samples.
• Trajectory inference (tree-shaped): Constructs tree-shaped trajectories using a minimum spanning tree algorithm to represent developmental or differentiation pathways.
• Pseudotime estimation: Estimates pseudotime using a graph-based algorithm to infer temporal progression and intermediate cellular states.
• Dimensionality reduction and unsupervised clustering: Integrates commonly used dimensionality reduction techniques and unsupervised clustering methods for single-cell data simplification and grouping.
• Single-cell-based algorithms: Employs algorithms tailored to single-cell cytometry data to analyze cellular heterogeneity and dynamics.

Scientific Applications:

• Heterogeneity-focused cytology: Analysis of cellular heterogeneity in complex biological samples using flow and mass cytometry data.
• Differentiation and reprogramming experiments: Reconstruction of developmental or reprogramming trajectories and identification of intermediate states in differentiation studies.
• Mass cytometry and time-course flow cytometry datasets: Application to high-dimensional mass cytometry and longitudinal flow cytometry datasets for temporal and population analyses.
• Cellular dynamics and developmental biology: Investigation of dynamic cellular processes and developmental pathways via trajectory and pseudotime analyses.

Methodology:

Applies single-cell-based algorithms combining dimensionality reduction, unsupervised clustering, minimum spanning tree–based tree construction, and graph-based pseudotime estimation, producing heuristic trajectory and pseudotime results for cytometry data.