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Updated: Dec 31, 2025

A Simple, Robust, and High Throughput Single Molecule Flow Stretching Assay Implementation for Studying Transport of Molecules Along DNA

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FLOW-MAP: a graph-based, force-directed layout algorithm for trajectory mapping in single-cell time course datasets.

Melissa E Ko¹, Corey M Williams^2,3, Kristen I Fread²

¹Cancer Biology Program, Stanford School of Medicine, Stanford, CA, USA.

Nature Protocols

|January 15, 2020

Summary

This summary is machine-generated.

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.

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Last Updated: Dec 31, 2025

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Published on: September 5, 2019

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.