SCAN-IT: Domain segmentation of spatial transcriptomics images by graph neural network.
SCAN-IT: Domain segmentation of spatial transcriptomics images by graph neural network.
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发表时间：
2021-11
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通讯作者：
Zhang J
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作者：
Cang Z;Ning X;Nie A;Xu M;Zhang J
复杂的生物组织由众多细胞以高度协调的方式组成，并执行各种生物功能。因此，将组织分割成空间和功能域对于理解和控制生物功能至关重要。新兴的空间转录组学技术允许同时测量具有精确空间信息的数千个基因，为解剖生物组织提供了前所未有的机会。然而，如何利用这样的噪声，稀疏，高维数据的组织分割仍然是一个重大的挑战。在这里，我们开发了一种基于深度学习的方法，称为SCAN-IT，将空间域识别问题转化为图像分割问题，细胞模仿像素和细胞内代表颜色通道的基因表达值。具体来说，SCAN-IT依赖于几何建模、图形神经网络和信息学方法DeepGraphInfomax。我们证明了SCAN-IT可以处理来自广泛的空间转录组学技术的数据集，包括具有高空间分辨率但低基因覆盖率的数据集以及具有低空间分辨率但高基因覆盖率的数据集。我们表明，SCAN-IT优于国家的最先进的方法，使用基准数据集与地面真域注释。
Complex biological tissues consist of numerous cells in a highly coordinated manner and carry out various biological functions. Therefore, segmenting a tissue into spatial and functional domains is critically important for understanding and controlling the biological functions. The emerging spatial transcriptomics technologies allow simultaneous measurements of thousands of genes with precise spatial information, providing an unprecedented opportunity for dissecting biological tissues. However, how to utilize such noisy, sparse, and high dimensional data for tissue segmentation remains a major challenge. Here, we develop a deep learning-based method, named SCAN-IT by transforming the spatial domain identification problem into an image segmentation problem, with cells mimicking pixels and expression values of genes within a cell representing the color channels. Specifically, SCAN-IT relies on geometric modeling, graph neural networks, and an informatics approach, DeepGraphInfomax. We demonstrate that SCAN-IT can handle datasets from a wide range of spatial transcriptomics techniques, including the ones with high spatial resolution but low gene coverage as well as those with low spatial resolution but high gene coverage. We show that SCAN-IT outperforms state-of-the-art methods using a benchmark dataset with ground truth domain annotations.