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
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
123Informations de copyright
© 2022. The Author(s).
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