Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution

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. This work presents a comprehensive benchmarking analysis of computational methods that integrates spatial and single-cell transcriptomics data for transcript distribution prediction and cell type deconvolution.

Automated summary

This study compares 16 integration methods using 45 paired datasets and 32 simulated datasets, finding that Tangram, gimVI, and SpaGE perform best in predicting RNA transcript distribution, while Cell2location, SpatialDWLS, and RCTD are top-performing methods for cell type deconvolution. The benchmark pipeline provided in the study helps researchers in selecting optimal integration methods for their datasets. The comprehensive benchmarking analysis presented in this work evaluates computational methods integrating spatial and single-cell transcriptomics data for transcript distribution prediction and cell type deconvolution.