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Updated: May 12, 2025

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

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Published on: December 12, 2019

CellRomeR: an R package for clustering cell migration phenotypes from microscopy data.

Iivari Kleino¹, Mats Perk¹, António G G Sousa¹

¹Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, FI-20520, Finland.

Bioinformatics Advances

|May 7, 2025

Summary

This summary is machine-generated.

The R package CellRomeR analyzes cell migration by clustering cells based on morphology and motility. This tool reveals distinct cellular phenotypes and migration patterns, enhancing understanding of cell population heterogeneity.

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Last Updated: May 12, 2025

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Area of Science:

Cell biology
Bioinformatics
Image analysis

Background:

Cell migration analysis often focuses on track characteristics, overlooking cell morphology and intensity heterogeneity.
Existing methods struggle to capture the full spectrum of cell behavior during migration.

Purpose of the Study:

To introduce CellRomeR, an R package for phenotypic clustering of cells based on microscopy image features.
To enable the identification and characterization of heterogeneity within migrating cell populations.
To link distinct cellular phenotypes with specific migration behaviors.

Main Methods:

Development of the CellRomeR R package.
Application of machine learning and iterative clustering projection for phenotypic analysis.
Clustering of cells along migration tracks to associate phenotypes with migration types.

Main Results:

CellRomeR facilitates phenotypic clustering of cells using morphological and motility features.
The package successfully identifies distinct cellular phenotypes and their associated migration patterns.
It enables the detection of migration behaviors linked to stable and unstable cell phenotypes.

Conclusions:

CellRomeR provides a user-friendly approach to analyze cell migration heterogeneity.
The package enhances the understanding of cellular behavior by integrating morphology and motility data.
It addresses limitations in clustering cell trajectories from microscopy data.