Systematic evaluation of robustness of deconvolution methods for spatial transcriptomics data in case of cell type mismatch.
5 affiliations
- 1Epidemiology and Data Science, Amsterdam UMC location University of Amsterdam, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands.
- 2Amsterdam Public Health, Methodology, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands.
- 3Amsterdam institute for Immunology & Infectious Diseases, Inflammatory Diseases, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands.
- 4Rheumatology and Clinical Immunology, Amsterdam UMC location University of Amsterdam, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands.
- 5Amsterdam Rheumatology & Immunology Center (ARC), Meibergdreef 9, 1105AZ Amsterdam, The Netherlands.
Abstract
Sequencing-based spatial transcriptomics (ST) preserves spatial information but with limited cellular resolution, whereas single-cell RNA-sequencing (scRNA-seq) provides single-cell resolution but loses spatial context during tissue dissociation. Given these complementary strengths, computational tools combine scRNA-seq and ST data using deconvolution to identify cell types and estimate their proportions at each spatial location using scRNA-seq reference data. However, these methods are sensitive to missing cell types in the reference data, a problem known as cell type mismatch. Using two reference datasets, we performed extensive simulations to systematically evaluate the robustness to cell type mismatch of six deconvolution methods (CARD, cell2location, RCTD, Seurat, SPOTlight, and Stereoscope) tailored for ST data, and two designed for bulk RNA-seq data (MuSiC, SCDC). At baseline, i.e. with no cell types missing from the reference datasets, cell2location showed the strongest performance, while Seurat performed the worst. By simulating different cell type mismatch scenarios, we found that the performance of deconvolution methods decreases proportionally to the number of cell types missing from the reference. Moreover, compared to baseline, the relative decrease in performance is similar across methods and therefore no method is more robust to cell type mismatch than others. Additionally, methods that perform well at baseline tend to assign the proportions of a missing cell type to the transcriptionally most similar cell types present in the reference data. Our results highlight the adverse effects of cell type mismatch on the performance of deconvolution methods for ST data and stress the need for more robust approaches to this issue.
Related
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BibTeX
@article{mahamune2026systematic,
title = {{Systematic evaluation of robustness of deconvolution methods for spatial transcriptomics data in case of cell type mismatch.}},
author = {Utkarsh M Mahamune and Aldo Jongejan and Antoine H C van Kampen and Lisa G M van Baarsen and Perry D Moerland},
journal = {Briefings in bioinformatics},
year = {2026},
month = sep,
doi = {10.1093/bib/bbag594},
url = {https://europepmc.org/article/MED/42949539},
note = {CC BY},
}
RIS
TY - JOUR
TI - Systematic evaluation of robustness of deconvolution methods for spatial transcriptomics data in case of cell type mismatch.
AU - Utkarsh M Mahamune
AU - Aldo Jongejan
AU - Antoine H C van Kampen
AU - Lisa G M van Baarsen
AU - Perry D Moerland
PY - 2026
DA - 2026/09/01
JO - Briefings in bioinformatics
DO - 10.1093/bib/bbag594
UR - https://europepmc.org/article/MED/42949539
AB - Sequencing-based spatial transcriptomics (ST) preserves spatial information but with limited cellular resolution, whereas single-cell RNA-sequencing (scRNA-seq) provides single-cell resolution but loses spatial context during tissue dissociation. Given these complementary strengths, computational tools combine scRNA-seq and ST data using deconvolution to identify cell types and estimate their proportions at each spatial location using scRNA-seq reference data. However, these methods are sensitive to missing cell types in the reference data, a problem known as cell type mismatch. Using two reference datasets, we performed extensive simulations to systematically evaluate the robustness to cell type mismatch of six deconvolution methods (CARD, cell2location, RCTD, Seurat, SPOTlight, and Stereoscope) tailored for ST data, and two designed for bulk RNA-seq data (MuSiC, SCDC). At baseline, i.e. with no cell types missing from the reference datasets, cell2location showed the strongest performance, while Seurat performed the worst. By simulating different cell type mismatch scenarios, we found that the performance of deconvolution methods decreases proportionally to the number of cell types missing from the reference. Moreover, compared to baseline, the relative decrease in performance is similar across methods and therefore no method is more robust to cell type mismatch than others. Additionally, methods that perform well at baseline tend to assign the proportions of a missing cell type to the transcriptionally most similar cell types present in the reference data. Our results highlight the adverse effects of cell type mismatch on the performance of deconvolution methods for ST data and stress the need for more robust approaches to this issue.
SN - 1467-5463
SN - 1477-4054
ER -