Researchers from Laura Elo’s group, have developed a new computational method to interpret complex single-cell data. The method helps researchers identify and group cell types across samples.

“We were inspired by the process of assembling a puzzle, where one begins by grouping pieces based on low- to high-level features, such as colour and shading, before looking at shape and patterns. Similarly, our algorithm progressively integrates cellular identities through multiple rounds of divisive clustering,” explains Doctoral Researcher António Sousa, the lead developer of Coralysis.

Coralysis has been implemented as an open-source software. At its core, it relies on machine learning, enabling it to build models that can be used to predict cellular identities in new datasets and even estimate how confident the predictions are. This helps researchers avoid the cumbersome and often unreliable task of manually identifying cell types. Another unique feature of Coralysis is its ability to detect changing cellular states that might otherwise be missed.

“Coralysis provides the scientific community with a new way to study cellular diversity and gain a deeper understanding of complex single-cell data. By making it openly available, we hope to support collaboration and accelerate discoveries across the global research community,” says Professor Laura Elo, the principal investigator of the project.

The study by Elo’s research group has been published in the scientific journal Nucleic Acids Research.

Contacts

Antonio Goncalves de Sousa, Doctoral Researcher, antonio.goncalvesdesousa@utu.fi

Sini Junttila, Associate Professor, simaju@utu.fi

Laura Elo, Professor, Principal Investigator, laura.elo@utu.fi

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