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

Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation-the assignment of cell type or cell state to each seque...

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Bibliographic Details
Published in:Genome Biology 2022-05, Vol.23 (1), p.123-123, Article 123
Main Authors: Dohmen, Jan, Baranovskii, Artem, Ronen, Jonathan, Uyar, Bora, Franke, Vedran, Akalin, Altuna
Format: Article
Language:English
Subjects:
Accuracy
Annotations
Cancer
Cell Count
Cell type classification
cellular microenvironment
Colorectal cancer
data collection
Datasets
Experiments
Genes
genome
Head & neck cancer
Kinases
Learning algorithms
Lung cancer
Machine Learning
Machine learning
Medical research
Method
Microenvironments
neoplasms
Single-cell genomics
Tumor cells
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Description
Summary:Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation-the assignment of cell type or cell state to each sequenced cell-is a challenge, especially identifying tumor cells within single-cell or spatial sequencing experiments. Here, we propose ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single-cell level. We test ikarus on multiple single-cell datasets, showing that it achieves high sensitivity and specificity in multiple experimental contexts.
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-022-02683-1