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Fischer, F. ; Fischer, D.S. ; Mukhin, R.* ; Isaev, A.* ; Biederstedt, E.* ; Villani, A.C.* ; Theis, F.J.

scTab: Scaling cross-tissue single-cell annotation models.

Nat. Commun. 15:6611 (2024)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Identifying cellular identities is a key use case in single-cell transcriptomics. While machine learning has been leveraged to automate cell annotation predictions for some time, there has been little progress in scaling neural networks to large data sets and in constructing models that generalize well across diverse tissues. Here, we propose scTab, an automated cell type prediction model specific to tabular data, and train it using a novel data augmentation scheme across a large corpus of single-cell RNA-seq observations (22.2 million cells). In this context, we show that cross-tissue annotation requires nonlinear models and that the performance of scTab scales both in terms of training dataset size and model size. Additionally, we show that the proposed data augmentation schema improves model generalization. In summary, we introduce a de novo cell type prediction model for single-cell RNA-seq data that can be trained across a large-scale collection of curated datasets and demonstrate the benefits of using deep learning methods in this paradigm.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Journal Nature Communications
Quellenangaben Volume: 15, Issue: 1, Pages: , Article Number: 6611 Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)