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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Autores principales: Dohmen, Jan, Baranovskii, Artem, Ronen, Jonathan, Uyar, Bora, Franke, Vedran, Akalin, Altuna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9150321/
https://www.ncbi.nlm.nih.gov/pubmed/35637521
http://dx.doi.org/10.1186/s13059-022-02683-1
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author Dohmen, Jan
Baranovskii, Artem
Ronen, Jonathan
Uyar, Bora
Franke, Vedran
Akalin, Altuna
author_facet Dohmen, Jan
Baranovskii, Artem
Ronen, Jonathan
Uyar, Bora
Franke, Vedran
Akalin, Altuna
author_sort Dohmen, Jan
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02683-1.
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spelling pubmed-91503212022-05-31 Identifying tumor cells at the single-cell level using machine learning Dohmen, Jan Baranovskii, Artem Ronen, Jonathan Uyar, Bora Franke, Vedran Akalin, Altuna Genome Biol Method 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02683-1. BioMed Central 2022-05-30 /pmc/articles/PMC9150321/ /pubmed/35637521 http://dx.doi.org/10.1186/s13059-022-02683-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Dohmen, Jan
Baranovskii, Artem
Ronen, Jonathan
Uyar, Bora
Franke, Vedran
Akalin, Altuna
Identifying tumor cells at the single-cell level using machine learning
title Identifying tumor cells at the single-cell level using machine learning
title_full Identifying tumor cells at the single-cell level using machine learning
title_fullStr Identifying tumor cells at the single-cell level using machine learning
title_full_unstemmed Identifying tumor cells at the single-cell level using machine learning
title_short Identifying tumor cells at the single-cell level using machine learning
title_sort identifying tumor cells at the single-cell level using machine learning
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9150321/
https://www.ncbi.nlm.nih.gov/pubmed/35637521
http://dx.doi.org/10.1186/s13059-022-02683-1
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