Protocol for deep-learning-driven cell type label transfer in single-cell RNA sequencing data

Protocol for deep-learning-driven cell type label transfer in single-cell RNA sequencing data

Summary: Here, we present a protocol for using SIMS (scalable, interpretable machine learning for single cell) to transfer cell type labels in single-cell RNA sequencing data. This protocol outlines data preparation, model training with labeled data or inference using pretrained models, and methods...

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Bibliographic Details
Main Authors: Zoe Zabetian, Jesus Gonzalez-Ferrer, Julian Lehrer, Vanessa D. Jonsson, Mircea Teodorescu, David Haussler, Mohammed A. Mostajo-Radji
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:STAR Protocols
Subjects:
Developmental biology
Sequencing
RNA-seq
Online Access:http://www.sciencedirect.com/science/article/pii/S2666166725001741
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Summary:Summary: Here, we present a protocol for using SIMS (scalable, interpretable machine learning for single cell) to transfer cell type labels in single-cell RNA sequencing data. This protocol outlines data preparation, model training with labeled data or inference using pretrained models, and methods for visualizing, downloading, and interpreting predictions. We provide stepwise instructions for accessing SIMS through the application programming interface (API), GitHub Codespaces, and a web application.For complete details on the use and execution of this protocol, please refer to Gonzalez-Ferrer et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
ISSN:2666-1667

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