Related Experiment Video
Updated: Dec 31, 2025
12:05
A Simple, Robust, and High Throughput Single Molecule Flow Stretching Assay Implementation for Studying Transport of Molecules Along DNA
Published on: October 1, 2017
8.5K
FLOW-MAP: a graph-based, force-directed layout algorithm for trajectory mapping in single-cell time course datasets
Melissa E Ko1, Corey M Williams2,3, Kristen I Fread2
1Cancer Biology Program, Stanford School of Medicine, Stanford, CA, USA.
Nature Protocols
|January 15, 2020
Summary
FLOW-MAP is a new software tool that visualizes cell changes over time in complex single-cell data. It helps researchers track cell populations and dynamics in experiments like stem cell differentiation.
Area of Science:
- Single-cell biology
- Computational biology
- Bioinformatics
Background:
- High-dimensional single-cell technologies offer discovery potential but pose analytical challenges.
- Analyzing dynamic changes in cell populations across time points in single-cell data is complex.
Purpose of the Study:
- To develop and describe FLOW-MAP, a graphical user interface (GUI)-based software tool for analyzing single-cell time course datasets.
- To provide a user-friendly method for visualizing cellular trajectories and identifying cell population dynamics.
Main Methods:
- Developed FLOW-MAP, a GUI-based tool utilizing graph layout analysis and sequential time ordering.
- Created the open-source R package FLOWMAPR, accessible via GUI or text commands.
- Applied FLOW-MAP to mass cytometry and single-cell RNA sequencing (scRNAseq) time course datasets.
Main Results:
- Demonstrated FLOW-MAP's ability to visualize cellular trajectories in high-dimensional single-cell data.
- Successfully applied the tool to analyze mouse embryonic stem cell differentiation and a published scRNAseq dataset.
- Compared FLOW-MAP with other methods, highlighting its advantages for specific dynamic processes.
Conclusions:
- FLOW-MAP provides an effective approach for analyzing dynamic processes in single-cell experiments.
- The tool facilitates the study of stem cell differentiation, development, oncogenesis, and drug resistance.
- FLOWMAPR offers a versatile and efficient solution for single-cell time course data analysis.
