Machine learning for cell classification and neighborhood analysis in glioma tissue.
cell classification
community analysis
machine learning
multiplex immunofluorescence
Journal
Cytometry. Part A : the journal of the International Society for Analytical Cytology
ISSN: 1552-4930
Titre abrégé: Cytometry A
Pays: United States
ID NLM: 101235694
Informations de publication
Date de publication:
12 2021
12 2021
Historique:
revised:
28
04
2021
received:
25
02
2021
accepted:
25
05
2021
pubmed:
6
6
2021
medline:
1
2
2022
entrez:
5
6
2021
Statut:
ppublish
Résumé
Multiplexed and spatially resolved single-cell analyses that intend to study tissue heterogeneity and cell organization invariably face as a first step the challenge of cell classification. Accuracy and reproducibility are important for the downstream process of counting cells, quantifying cell-cell interactions, and extracting information on disease-specific localized cell niches. Novel staining techniques make it possible to visualize and quantify large numbers of cell-specific molecular markers in parallel. However, due to variations in sample handling and artifacts from staining and scanning, cells of the same type may present different marker profiles both within and across samples. We address multiplexed immunofluorescence data from tissue microarrays of low-grade gliomas and present a methodology using two different machine learning architectures and features insensitive to illumination to perform cell classification. The fully automated cell classification provides a measure of confidence for the decision and requires a comparably small annotated data set for training, which can be created using freely available tools. Using the proposed method, we reached an accuracy of 83.1% on cell classification without the need for standardization of samples. Using our confidence measure, cells with low-confidence classifications could be excluded, pushing the classification accuracy to 94.5%. Next, we used the cell classification results to search for cell niches with an unsupervised learning approach based on graph neural networks. We show that the approach can re-detect specialized tissue niches in previously published data, and that our proposed cell classification leads to niche definitions that may be relevant for sub-groups of glioma, if applied to larger data sets.
Identifiants
pubmed: 34089228
doi: 10.1002/cyto.a.24467
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1176-1186Subventions
Organisme : Stiftelsen för Strategisk Forskning
ID : BD150008
Organisme : Cancerfonden
Organisme : H2020 European Research Council
ID : 682810
Organisme : Vetenskapsrådet
Organisme : Sahlgrenska Akademin
Organisme : Göteborgs Universitet
Informations de copyright
© 2021 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.
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