Spatial transcriptome technologies have enabled the measurement of gene expression while maintaining spatial location information for deciphering the spatial heterogeneity of biological tissues. However, they were heavily limited by the sparse spatial resolution and low data quality. To this end, we develop a spatial location-supervised auto-encoder generator STAGE for generating high-density spatial transcriptomics (ST). STAGE takes advantage of the customized supervised auto-encoder to learn continuous patterns of gene expression in space and generate high-resolution expressions for given spatial coordinates. STAGE can improve the low quality of spatial transcriptome data and smooth the generated manifold of gene expression through the de-noising function on the latent codes of the auto-encoder. Applications to four ST datasets, STAGE has shown better recovery performance for down-sampled data than existing methods, revealed significant tissue structure specificity, and enabled robust identification of spatially informative genes and patterns. In addition, STAGE can be extended to three-dimensional (3D) stacked ST data for generating gene expression at any position between consecutive sections for shaping high-density 3D ST configuration.

中文翻译：

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使用 STAGE 高密度生成空间转录组学

空间转录组技术使得基因表达的测量成为可能，同时保留空间位置信息以破译生物组织的空间异质性。然而，它们受到稀疏空间分辨率和低数据质量的严重限制。为此，我们开发了一种空间位置监督自动编码器生成器 STAGE，用于生成高密度空间转录组学（ST）。 STAGE 利用定制的监督自动编码器来学习空间中基因表达的连续模式，并为给定的空间坐标生成高分辨率的表达。 STAGE可以通过对自动编码器的潜在编码进行去噪功能，改善空间转录组数据的低质量，并平滑生成的基因表达流形。在四个 ST 数据集的应用中，STAGE 显示出比现有方法更好的下采样数据恢复性能，揭示了显着的组织结构特异性，并能够稳健地识别空间信息基因和模式。此外，STAGE 可以扩展到三维 (3D) 堆叠 ST 数据，用于在连续切片之间的任何位置生成基因表达，从而形成高密度 3D ST 配置。