Graduate School of Biomedical Sciences, Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA.
Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA.
Nat Commun. 2022 Nov 14;13(1):6912. doi: 10.1038/s41467-022-34567-0.
Spatially resolved transcriptomics is a relatively new technique that maps transcriptional information within a tissue. Analysis of these datasets is challenging because gene expression values are highly sparse due to dropout events, and there is a lack of tools to facilitate in silico detection and annotation of regions based on their molecular content. Therefore, we develop a computational tool for detecting molecular regions and region-based Missing value Imputation for Spatially Transcriptomics (MIST). We validate MIST-identified regions across multiple datasets produced by 10x Visium Spatial Transcriptomics, using manually annotated histological images as references. We benchmark MIST against a spatial k-nearest neighboring baseline and other imputation methods designed for single-cell RNA sequencing. We use holdout experiments to demonstrate that MIST accurately recovers spatial transcriptomics missing values. MIST facilitates identifying intra-tissue heterogeneity and recovering spatial gene-gene co-expression signals. Using MIST before downstream analysis thus provides unbiased region detections to facilitate annotations with the associated functional analyses and produces accurately denoised spatial gene expression profiles.
空间分辨转录组学是一种相对较新的技术，可在组织内绘制转录信息。由于缺失事件，基因表达值高度稀疏，并且缺乏基于其分子内容在计算机上检测和注释区域的工具，因此分析这些数据集具有挑战性。因此，我们开发了一种用于检测分子区域和基于区域的空间转录组学缺失值插补的计算工具（MIST）。我们使用手动注释的组织学图像作为参考，在多个由 10x Visium 空间转录组学生成的数据集上验证了 MIST 识别的区域。我们将 MIST 与空间 k-最近邻基线和其他专为单细胞 RNA 测序设计的插补方法进行基准测试。我们使用保留实验来证明 MIST 可以准确地恢复空间转录组学中的缺失值。MIST 有助于识别组织内异质性并恢复空间基因-基因共表达信号。因此，在下游分析之前使用 MIST 可以提供无偏的区域检测，以便进行相关的功能分析注释，并生成准确去噪的空间基因表达谱。
