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.
| Original language | English |
|---|---|
| Article number | vbaf069 |
| Journal | Bioinformatics Advances |
| Volume | 5 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 4 Apr 2025 |
| MoE publication type | A1 Journal article-refereed |
Funding
The authors wish to thank Dr Olof Rundquist for his contributions in the CellRomeR workshop. L.L.E. reports grants from the European Union's Horizon 2020 Research and Innovation Programme (955321), Academy of Finland (310561, 329278, 335434, 335611, 341342, and 364700), Sigrid Juselius Foundation, and Cancer Foundation Finland during the conduct of the study. Our research is also supported by University of Turku Graduate School (UTUGS), Biocenter Finland, and ELIXIR Finland. A.G.G.S. was supported by the European Union's Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant Agreement No. 955321. M.L. has been supported by the Vilho, Yrjö and Kalle Väisälä Foundation. L.L.E. reports grants from the European Union's Horizon 2020 Research and Innovation Programme (955321), Academy of Finland (310561, 329278, 335434, 335611, 341342, and 364700), Sigrid Juselius Foundation, and Cancer Foundation Finland during the conduct of the study. Our research is also supported by University of Turku Graduate School (UTUGS), Biocenter Finland, and ELIXIR Finland. A.G.G.S. was supported by the European Union's Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant Agreement No. 955321. M.L. has been supported by the Vilho, Yrjö and Kalle Väisälä Foundation.