Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution
Abstract
Spatial transcriptomics approaches have substantially advanced our capacity to detect the spatial distribution of RNA transcripts in tissues, yet it remains challenging to characterize whole-transcriptome-level data for single cells in space. Addressing this need, researchers have developed integration methods to combine spatial transcriptomic data with single-cell RNA-seq data to predict the spatial distribution of undetected transcripts and/or perform cell type deconvolution of spots in histological sections. However, to date, no independent studies have comparatively analyzed these integration methods to benchmark their performance. Here we present benchmarking of 16 integration methods using 45 paired datasets (comprising both spatial transcriptomics and scRNA-seq data) and 32 simulated datasets. We found that Tangram, gimVI, and SpaGE outperformed other integration methods for predicting the spatial distribution of RNA transcripts, whereas Cell2location, SpatialDWLS, and RCTD are the top-performing methods for the cell type deconvolution of spots. We provide a benchmark pipeline to help researchers select optimal integration methods to process their datasets.
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Tangram outperformed other imputation methods such as gimVI and SpaGE with respect to various accuracy metrics and scalability.
“It was shown to outperform other imputation methods such as gimVI225 and SpaGE226 with respect to various accuracy metrics and scalability215.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [215]
On real data sets, SpatialDWLS and RCTD performed best in overall accuracy among cell-type deconvolution methods.
“For simulated data sets, cell2location outperformed other approaches for cell-type deconvolution, but requires more computational resources, whereas for real data sets, SpatialDWLS and RCTD performed best in terms of the overall accuracy score based on four different accuracy metrics215,216.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [215]
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Vitamin D sufficiency enhances differentiation of patient-derived prostate epithelial organoids
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Hu, Y., Li, B., Zhang, W., Liu, N., Cai, P., Chen, F., & Qu, K. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab085 · Cited by 24 · see citing works
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Hu, Y., Li, B., Zhang, W., Liu, N., Cai, P., Chen, F., & Qu, K. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/864488 · Cited by 8 · see citing works
Self-citationVitamin D sufficiency enhances differentiation of patient-derived prostate epithelial organoids
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Best practices for single-cell analysis across modalities
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“…, for example, leverage spatial correlation testing, BayesSpace 227 uses Markov random fields, spaGCN 229 uses graph neural networks to integrate gene expression data, spatial information and histology images, and sepal 235 utilizes diffusion-based modelling to identify genes with spatial patterns.”
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Self-citationSpatially resolved multiomics of human cardiac niches
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A visual–omics foundation model to bridge histopathology with spatial transcriptomics
Chen, W., Zhang, P., Tran, T. N., Xiao, Y., Li, S., Shah, V. V., Cheng, H., Brannan, K. W., Youker, K., Lai, L., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02707-1 · Cited by 118 · see citing works
“In summary, Loki Annotate effectively anno-tates H&E images by using tissue-type-specific bulk RNA-seq data as a reference.”
Benchmarking and integration of methods for deconvoluting spatial transcriptomic data
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Evaluating the Utilities of Foundation Models in Single-cell Data Analysis
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Lin, W., Li, Y., Qiu, C., Zou, B., Gong, Y., Zhang, X., Tian, D., Sherman, W., Sanchez, F., Wu, D., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1298 · Cited by 46 · see citing works
Self-citation
- Authors
- Li, B., Zhang, W., Guo, C., Xu, H., Li, L., Fang, M., Hu, Y., Zhang, X., Yao, X., Tang, M., Liu, K., Zhao, X., Lin, J., Cheng, L., Chen, F., Xue, T., & Qu, K.
- Affiliations
- University of Science and Technology of China (CN)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Qu, Kun
- ORCID
- Venue
- Nature Methods
- Published
- 2022-05-16
- Volume
- 19
- Issue
- 6
- Pages
- 662-670
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W4280533477
- Cited by
- 459
- References
- 89
- Citations by year
- 2026: 96 · 2025: 111 · 2024: 149 · 2023: 82 · 2022: 20 · 2021: 1
- FWCI
- 31.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (T2125012, 91940306, 31970858, 31771428, 32170668)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- spatial transcriptomics, single-cell RNA sequencing, multi-omics integration, benchmarking, cell type deconvolution, tangram, simulated data
- MeSH terms
- RNA, Sequence Analysis, RNA, Benchmarking, Single-Cell Analysis, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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