Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21211, United States.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, United States.
Nat Commun. 2022 Apr 29;13(1):2339. doi: 10.1038/s41467-022-30033-z.
Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type-specific spatial patterns and gene expression variation. To address this challenge, we develop STdeconvolve as a reference-free approach to deconvolve underlying cell types comprising such multi-cellular pixel resolution spatial transcriptomics (ST) datasets. Using simulated as well as real ST datasets from diverse spatial transcriptomics technologies comprising a variety of spatial resolutions such as Spatial Transcriptomics, 10X Visium, DBiT-seq, and Slide-seq, we show that STdeconvolve can effectively recover cell-type transcriptional profiles and their proportional representation within pixels without reliance on external single-cell transcriptomics references. STdeconvolve provides comparable performance to existing reference-based methods when suitable single-cell references are available, as well as potentially superior performance when suitable single-cell references are not available. STdeconvolve is available as an open-source R software package with the source code available at https://github.com/JEFworks-Lab/STdeconvolve .
最近的技术进步使得能够进行空间分辨转录组分析，但在多细胞像素分辨率下，这阻碍了细胞类型特异性空间模式和基因表达变化的识别。为了解决这一挑战，我们开发了 STdeconvolve，这是一种无参考的方法，可以对包含这种多细胞像素分辨率空间转录组学（ST）数据集的底层细胞类型进行去卷积。我们使用模拟和真实的 ST 数据集，这些数据集来自各种空间转录组学技术，包括各种空间分辨率，如 Spatial Transcriptomics、10X Visium、DBiT-seq 和 Slide-seq，我们表明 STdeconvolve 可以有效地恢复细胞类型的转录谱及其在像素中的比例表示，而无需依赖外部单细胞转录组学参考。当有合适的单细胞参考时，STdeconvolve 提供与现有基于参考的方法相当的性能，而当没有合适的单细胞参考时，它可能具有更好的性能。STdeconvolve 是一个开源的 R 软件包，可以在 https://github.com/JEFworks-Lab/STdeconvolve 上获得源代码。
