Evidence map›Paper›PMID 40330627›Full record

ArticleBioinformatics advances2025

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

Iivari Kleino, Mats Perk, António G G Sousa, Markus Linden, Julia Mathlin, Daniel Giesel, Paulina Frolovaite, Sami Pietilä, Sini Junttila, Tomi Suomi and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Iivari KleinoTurku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, FI-20520, Finland.ORCID https://orcid.org/0000-0001-6219-8575
Mats PerkTurku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, FI-20520, Finland.
António G G SousaTurku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, FI-20520, Finland.
Markus LindenTurku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, FI-20520, Finland.
Julia MathlinTurku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, FI-20520, Finland.
Daniel GieselTurku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, FI-20520, Finland.
Paulina FrolovaiteTurku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, FI-20520, Finland.
Sami PietiläTurku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, FI-20520, Finland.
Sini JunttilaTurku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, FI-20520, Finland.ORCID https://orcid.org/0000-0003-3754-5584
Tomi SuomiTurku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, FI-20520, Finland.ORCID https://orcid.org/0000-0003-3639-979X
Laura L EloTurku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, FI-20520, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID40330627
PMCPMC12052403

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.