FLOWMAPR

FLOWMAPR maps cellular trajectories in single-cell time-course datasets using a graph-based, force-directed layout with sequential time ordering to analyze dynamic processes from flow cytometry, mass cytometry, and scRNAseq data in R.

Key Features:

• Graph-based, force-directed layout: Uses a graph-based, force-directed layout algorithm tailored for trajectory mapping in single-cell time-course data.
• Sequential time ordering: Incorporates sequential time ordering to represent temporal progression of cellular states.
• High-dimensional data handling: Processes high-dimensional single-cell datasets generated by flow cytometry, mass cytometry, and scRNAseq.
• Population identification and tracking: Identifies unique cell populations and tracks their changes across different time points.
• R package implementation: Implemented as an R package operating on processed data frames in R.
• Extensibility: Provides an extensible framework within R for single-cell trajectory analysis.

Scientific Applications:

• In vitro stem cell differentiation: Maps trajectories and state transitions during in vitro differentiation of stem cells.
• In vivo developmental studies: Reconstructs temporal progression of cell states in developmental biology studies.
• Oncogenesis research: Characterizes tumor cell state dynamics and transitions relevant to cancer development.
• Investigation of drug resistance emergence: Tracks emergence and progression of resistant cell populations over time.
• Analysis of cell signaling dynamics: Visualizes temporal changes in signaling-related cellular phenotypes.

Methodology:

FLOWMAPR applies a graph-based, force-directed layout algorithm with sequential time ordering to generate trajectory visualizations from high-dimensional single-cell datasets.