小柯机器人

中国科学技术大学瞿昆团队完成多种空间转录组分析算法的系统性评估。相关论文于2022年5月16日在线发表在《自然—方法学》杂志上。

研究人员表示，空间转录组学方法大大推进了人们检测组织中RNA转录本的空间分布的能力，然而，在空间上对单细胞的全转录组水平数据进行表征仍然具有挑战性。

为了满足这一需求，研究人员开发了整合方法，将空间转录组数据与单细胞RNA-seq数据结合起来，从而预测未检测到的转录本的空间分布和/或对组织学切片中的斑点进行细胞类型解构。然而，到目前为止，还没有独立的研究对这些整合方法进行比较分析来衡量其性能。因此，研究人员使用45个配对数据集（包括空间转录组学和scRNA-seq数据）和32个模拟数据集对16种整合方法进行了基准测试。研究人员发现Tangram、gimVI和SpaGE在预测RNA转录本的空间分布方面优于其他整合方法，而Cell2location、SpatialDWLS和RCTD则是细胞类型去卷积的最佳方法。研究人员提供了一个基准管线，可以帮助研究人员选择最佳的整合方法来处理他们的数据集。

附：英文原文

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

Author: 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

Issue&Volume: 2022-05-16

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. 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.

DOI: 10.1038/s41592-022-01480-9

Source: https://www.nature.com/articles/s41592-022-01480-9

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex