Iivari Kleino, Mats Perk, António G G Sousa, Markus Linden, Julia Mathlin, Daniel Giesel, Paulina Frolovaite, Sami Pietilä, Sini Junttila, Tomi Suomi, Laura L Elo
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引用次数: 0
Abstract
Motivation: The analysis of cell migration using time-lapse microscopy typically focuses on track characteristics for classification and statistical evaluation of migration behaviour. However, considerable heterogeneity can be seen in cell morphology and microscope signal intensity features within the migrating cell populations.
Results: To utilize this information in cell migration analysis, we introduce here an R package CellRomeR, designed for the phenotypic clustering of cells based on their morphological and motility features from microscopy images. Utilizing machine learning techniques and building on an iterative clustering projection method, CellRomeR offers a new approach to identify heterogeneity in cell populations. The clustering of cells along the migration tracks allows association of distinct cellular phenotypes with different cell migration types and detection of migration patterns associated with stable and unstable cell phenotypes. The user-friendly interface of CellRomeR and multiple visualization options facilitate an in-depth understanding of cellular behaviour, addressing previous challenges in clustering cell trajectories using microscope cell tracking data.
Availability and implementation: CellRomeR is available as an R package from https://github.com/elolab/CellRomeR.
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