ArticleNature methods2022
Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution.
Article in Nature methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 286 papers.
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286 citing papers in PubMed.
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- CoxFormer enables spatial omics inference with multimodal generative modeling.Nature communications · 2026Article
- Φ-Space ST: A platform-agnostic method to identify cell states in spatial transcriptomics studies.Cell reports methods · 2026Article
- PRISM: Prior-enhanced Inference for Spatial Transcriptomic Cell Type Mapping.Bioinformatics (Oxford, England) · 2026Article
- Tissue and cellular spatiotemporal dynamics in colon aging.Nature biotechnology · 2026Article
- NicheTrans: spatial-aware cross-omics translation.Nature methods · 2026Article
- STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics.Nature communications · 2026Article
- Toward simultaneous pseudo-space reconstruction and cell-type deconvolution of single-cell spatial transcriptome using SpaDicer.Cell reports methods · 2026Article
- scDifformer: diffusion-based post-training for virtual cell modeling across large-scale single-cell data.Nucleic acids research · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Structural-information guided fusion for spatial domain identification from spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- R4ST: a reference-guided graph-generative model for robust reconstruction of spatial transcriptomic profiles.Bioinformatics (Oxford, England) · 2026Article
226 more citing papers are in PubMed but not listed here.
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17 authors.
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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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