SpaceWalker enables interactive gradient exploration for spatial transcriptomics data.
SpaceWalker enables interactive gradient exploration for spatial transcriptomics data.
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DOI：
10.1016/j.crmeth.2023.100645
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发表时间：
2023-12-18
期刊：
影响因子：
--
通讯作者：
Lelieveldt, Boudewijn
中科院分区：
文献类型：
--
作者：
Li, Chang;Thijssen, Julian;Kroes, Thomas;de Boer, Mitchell;Abdelaal, Tamim;Hoellt, Thomas;Lelieveldt, Boudewijn
在空间转录学(ST)数据中，生物相关的特征，如组织间隔或细胞状态转换，反映在基因表达梯度上。在这里，我们介绍了Space Walalker，这是一个可视化分析工具，用于探索2D和3D ST数据的局部梯度结构。用户可以由高维数据的本地固有维度引导以定义种子位置，根据该种子位置，基于高维数据拓扑，洪泛填充算法在运行中识别转录相似的细胞。在几个使用案例中，我们证明了这些被淹没的细胞的空间投影突出了组织结构特征，并且在空间和转录领域中对基因表达梯度的交互检索证实了已知的生物学。我们还表明，Space Walalker适用于几种不同的ST协议，在保持实时交互性能的同时，可以很好地扩展到大型、多层、3D全脑ST数据。一种可视化工具，用于在2D和3D中探索空间转录数据，即时检索组织结构和局部基因表达梯度标尺，以实时性能扩展到大型、多层、3D全脑数据，并跨空间转录切割协议通用空间转录切割(ST)使能够描绘组织切片中数百个基因的表达概况，精确到其组织环境中的单个细胞水平。ST数据的梯度结构对组织生物学特别感兴趣，因为空间基因表达梯度通常代表组织间隔边缘，而在单细胞转录领域，基因表达梯度可能代表细胞类型差异和平稳的表型转变。已经开发了各种计算方法来分别从空间域或基因表达域提取信息。然而，对单细胞和ST数据空间中表达梯度的综合生物学解释仍然具有挑战性。许多现有的ST分析管道是基于脚本的，缺乏交互探索设施，并且没有用于自动识别局部表达梯度的特定设施。Li等人。描述Space Walalker，这是一种可视化分析工具，用于探索2D和3D空间转录数据的组织结构和局部梯度结构。他们的工具使用户能够深入了解组织中的空间图案和相关的基因表达梯度。
In spatial transcriptomics (ST) data, biologically relevant features such as tissue compartments or cell-state transitions are reflected by gene expression gradients. Here, we present SpaceWalker, a visual analytics tool for exploring the local gradient structure of 2D and 3D ST data. The user can be guided by the local intrinsic dimensionality of the high-dimensional data to define seed locations, from which a flood-fill algorithm identifies transcriptomically similar cells on the fly, based on the high-dimensional data topology. In several use cases, we demonstrate that the spatial projection of these flooded cells highlights tissue architectural features and that interactive retrieval of gene expression gradients in the spatial and transcriptomic domains confirms known biology. We also show that SpaceWalker generalizes to several different ST protocols and scales well to large, multi-slice, 3D whole-brain ST data while maintaining real-time interaction performance. A visualization tool for exploring spatial transcriptomics data in 2D and 3D On-the-fly retrieval of tissue structure and localized gene expression gradients Scales to large, multi-slice, 3D whole-brain data with real-time performance Generalizes across spatial transcriptomics protocols Spatial transcriptomics (ST) enables profiling of the expression of hundreds of genes in tissue sections down to the level of single cells in their tissue environment. The gradient structure of ST data is particularly interesting for tissue biology because spatial gene expression gradients often represent tissue compartment edges, whereas in the single-cell transcriptomic domain, gene expression gradients may represent cell-type differences and smooth phenotypic transitions. Various computational approaches have been developed to extract information from either the spatial domain or the gene expression domain individually. However, integrative biological interpretation of expression gradients in single-cell and ST data spaces remains challenging. Many prior ST analysis pipelines are script based, lack interactive exploration facilities, and do not have specific facilities for automatic identification of localized expression gradients. Li et al. describe SpaceWalker, a visual analytics tool for exploring the tissue architecture and local gradient structure of spatial transcriptomics data in 2D and 3D. Their tool enables the user to gain insight into the spatial patterning and the associated gene expression gradients in the tissue.
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影响因子：
4.4
作者：
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