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by claude@2026-07, 2026-07-03
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The paper studies how robust deconvolution methods for sequencing-based spatial transcriptomics (ST)—which estimate cell-type proportions using scRNA-seq reference profiles—are to “cell type mismatch,” meaning relevant cell types are absent from the scRNA-seq reference. Using extensive simulations, the authors compared six ST-tailored deconvolution methods and two bulk RNA-seq deconvolution methods, finding that with no missing cell types (baseline) cell2location, RCTD, and CARD performed best while SPOTlight performed worst. When simulating missing-cell-type scenarios, deconvolution performance decreased in proportion to the number of missing cell types, and for most methods this decline was similar relative to baseline, with well-performing methods tending to reassign missing-cell-type proportions to transcriptionally most similar present cell types. The paper is a simulation-based evaluation, and its main limitation is that it may not capture all real-world complexities of tissue and reference generation. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
Sequencing-based spatial transcriptomics (ST) approaches preserve spatial information but with limited cellular resolution, whereas single-cell RNA-sequencing (scRNA-seq) techniques provide single-cell resolution but lose spatial context during tissue dissociation. Given these complementary strengths, computational tools have been developed to combine scRNA-seq and ST data. These methods use deconvolution techniques to identify cell types and estimate their proportions at each spatial location in ST data, using scRNA-seq reference data. However, these methods are sensitive to missing cell types in the scRNA-seq reference, 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, Stereoscope) tailored for ST data, and two designed for bulk RNA-seq data (MuSiC, SCDC). At baseline, that is, 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, for most methods the relative decrease in performance is similar. 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.
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Abstract
Sequencing-based spatial transcriptomics (ST) approaches preserve spatial information but with limited cellular resolution. Single-cell RNA-sequencing (scRNA-seq) techniques, on the other hand, provide single-cell resolution but lose spatial resolution because of the tissue dissociation step. With these complementary strengths in mind, computational tools have been developed to combine scRNA-seq and ST data. These approaches use deconvolution to identify cell types and their reoctive proportions present at each location in ST data, with the aid of a scRNA-seq reference dataset. It has been suggested that deconvolution methods are sensitive to the absence of cell types in the scRNA-seq reference, a problem referred to as cell type mismatch.
Here, we used extensive simulations to systematically evaluate the robustness to cell type mismatch of six state-of-the-art deconvolution methods tailored for spatially resolved transcriptomics data, along with two deconvolution methods designed for bulk RNA-seq data. At baseline, that is, with no cell types missing from the reference data, cell2location, RCTD, and CARD were the best performing methods, while SPOTlight performed worst. By simulating various cell type mismatch scenarios, we found that the performance of deconvolution methods decreases proportionally to the number of cell types missing from the reference data. Moreover, for most deconvolution methods the decrease in performance is similar relative to their baseline performance. We also observed that those 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.
This study highlights the adverse effects of cell type mismatch on the performance of deconvolution methods for ST data and stresses the need for methods that are more robust to this type of mismatch.
Competing Interest Statement
The authors have declared no competing interest.
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