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

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Item Type:	Article
Title:	Identifying tumor cells at the single-cell level using machine learning
Creators:	Dohmen, J. ORCID: https://orcid.org/0000-0002-7826-3025, Baranovskii, A. ORCID: https://orcid.org/0000-0001-7217-3881, Ronen, J. ORCID: https://orcid.org/0000-0003-3980-6469, Uyar, B. ORCID: https://orcid.org/0000-0002-3170-4890, Franke, V. ORCID: https://orcid.org/0000-0003-3606-6792 and Akalin, A. ORCID: https://orcid.org/0000-0002-0468-0117
Abstract:	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.
Keywords:	Single-Cell Genomics, Machine Learning, Cell Type Classification, Cancer
Source:	Genome Biology
ISSN:	1474-760X
Publisher:	BioMed Central
Volume:	23
Number:	1
Page Range:	123
Date:	30 May 2022
Official Publication:	https://doi.org/10.1186/s13059-022-02683-1
PubMed:	View item in PubMed

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