反褶积
转录组
计算机科学
计算生物学
稳健性(进化)
水准点(测量)
空间分析
生物
算法
基因
基因表达
地图学
数学
遗传学
地理
统计
作者
Zixiang Zhou,Yunshan Zhong,Zemin Zhang,Xianwen Ren
标识
DOI:10.1038/s41467-023-43600-9
摘要
Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmark Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics platforms and datasets and demonstrate the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Application to a human pancreatic cancer dataset reveals cancer-clone-specific T cell infiltration, and application to lymph node samples identifies differential cytotoxic T cells between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch.
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