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

• Published in: Nature methods Vol. 19; no. 6; pp. 662 - 670
• Main Authors: Li, Bin, Zhang, Wen, Guo, Chuang, Xu, Hao, Li, Longfei, Fang, Minghao, Hu, Yinlei, Zhang, Xinye, Yao, Xinfeng, Tang, Meifang, Liu, Ke, Zhao, Xuetong, Lin, Jun, Cheng, Linzhao, Chen, Falai, Xue, Tian, Qu, Kun
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.06.2022; Nature Publishing Group
• Subjects: 631/114/2401; 631/114/2416; 631/208/212/2019; 631/553; Analysis; Benchmarks; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Computer applications; Datasets; Deconvolution; Integration; Life Sciences; Proteomics; Ribonucleic acid; RNA; Spatial data; Spatial distribution; Transcriptomes; Transcriptomics
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/s41592-022-01480-9

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.