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

Cancer Cell type classification Machine learning Single-cell genomics

Journal

Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660

Informations de publication

Date de publication:
30 05 2022
Historique:
received: 23 11 2021
accepted: 06 05 2022
entrez: 31 5 2022
pubmed: 1 6 2022
medline: 3 6 2022
Statut: epublish

Résumé

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.

Identifiants

pubmed: 35637521
doi: 10.1186/s13059-022-02683-1
pii: 10.1186/s13059-022-02683-1
pmc: PMC9150321
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

123

Informations de copyright

© 2022. The Author(s).

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Auteurs

Jan Dohmen (J)

Bioinformatics 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.

Artem Baranovskii (A)

Non-coding RNAs and Mechanisms of Cytoplasmic Gene Regulation Lab, Berlin Institute for Medical Systems Biology, Hannoversche Str. 28, 10115, Berlin, Germany.
Free University Berlin, Kaiserswerther Str. 16-18, 14195, Berlin, Germany.

Jonathan Ronen (J)

Bioinformatics 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.

Bora Uyar (B)

Bioinformatics 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.

Vedran Franke (V)

Bioinformatics 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. vedran.franke@mdc-berlin.de.

Altuna Akalin (A)

Bioinformatics 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. altuna.akalin@mdc-berlin.de.

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