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Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics data
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Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics data
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Journal Article
Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics data
2025
Overview
Background
Imaging-based spatial transcriptomics technologies allow us to explore spatial gene expression profiles at the cellular level. Cell type annotation of imaging-based spatial data is challenging due to the small gene panel, but it is a crucial step for downstream analyses. Many good reference-based cell type annotation tools have been developed for single-cell RNA sequencing and sequencing-based spatial transcriptomics data. However, the performance of the reference-based cell type annotation tools on imaging-based spatial transcriptomics data has not been well studied yet.
Results
We compared performance of five reference-based methods (
SingleR
,
Azimuth
,
RCTD
,
scPred
and
scmapCell
) with the marker-gene-based manual annotation method on an imaging-based Xenium data of human breast cancer. A practical workflow has been demonstrated for preparing a high-quality single-cell RNA reference, evaluating the accuracy, and estimating the running time for reference-based cell type annotation tools.
Conclusions
SingleR
was the best performing reference-based cell type annotation tool for the Xenium platform, being fast, accurate and easy to use, with results closely matching those of manual annotation.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
/ Analysis
/ Biomedical and Life Sciences
/ Cells
/ Computational Biology/Bioinformatics
/ Computer Appl. in Life Sciences
/ Datasets
/ Female
/ Gene Expression Profiling - methods
/ Genes
/ Genomics
/ Humans
/ Imaging
/ Methods
/ Molecular Sequence Annotation - methods
/ RNA
/ Sequence Analysis, RNA - methods
/ Single-Cell Analysis - methods
/ Software
/ Workflow
/ Xenium
