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Related Experiment Video

Updated: Sep 21, 2025

Author Spotlight: Shear Assay Protocol for the Determination of Single-Cell Material Properties

Author Spotlight: Shear Assay Protocol for the Determination of Single-Cell Material Properties

Published on: May 19, 2023

Identifying tumor cells at the single-cell level using machine learning.

Jan Dohmen¹, Artem Baranovskii^2,3, Jonathan Ronen¹

¹Bioinformatics and Omics Data Science Platform, Berlin Institute For Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Hannoversche Str.28, 10115, Berlin, Germany.

|May 31, 2022

Summary

This summary is machine-generated.

Identifying cancerous cells in tumors is difficult. We developed ikarus, a machine learning tool that accurately distinguishes tumor cells from normal cells in single-cell sequencing data.

Keywords:

Cancer Cell type classification Machine learning Single-cell genomics

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Last Updated: Sep 21, 2025

Author Spotlight: Shear Assay Protocol for the Determination of Single-Cell Material Properties

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Published on: May 19, 2023

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

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Published on: August 16, 2020

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

Area of Science:

Cancer biology
Computational biology
Genomics

Background:

Tumors comprise cancerous cells and a complex microenvironment.
Single-cell sequencing offers detailed molecular insights into tumor tissues.
Accurate cell annotation, particularly for tumor cells, remains a significant challenge in single-cell and spatial sequencing.

Purpose of the Study:

To introduce ikarus, a novel machine learning pipeline.
To enable precise identification and annotation of tumor cells at the single-cell level.

Main Methods:

Development of the ikarus machine learning pipeline.
Testing ikarus on diverse single-cell sequencing datasets.
Evaluation of ikarus performance using sensitivity and specificity metrics.

Main Results:

ikarus demonstrates high accuracy in distinguishing tumor cells from normal cells.
The pipeline shows robust performance across various experimental settings.
Successful application of ikarus in multiple single-cell datasets.

Conclusions:

ikarus provides a reliable computational solution for tumor cell identification.
This tool enhances the analysis of single-cell and spatial sequencing data in cancer research.
Improved tumor cell annotation facilitates a deeper understanding of tumor biology.