Chitra Uthsav, Arnold Brian J, Sarkar Hirak, Ma Cong, Lopez-Darwin Sereno, Sanno Kohei, Raphael Benjamin J
Department of Computer Science, Princeton University, Princeton, NJ, USA.
Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA.
bioRxiv. 2023 Oct 13:2023.10.10.561757. doi: 10.1101/2023.10.10.561757.
Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of this data complicates the analysis of spatial gene expression patterns such as gene expression gradients. We address these issues by deriving a of a tissue slice-analogous to a map of elevation in a landscape-using a novel quantity called the . Contours of constant isodepth enclose spatial domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in gene expression. We develop GASTON, an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gene expression gradients, and piecewise linear functions of the isodepth that model both continuous gradients and discontinuous spatial variation in the expression of individual genes. We validate GASTON by showing that it accurately identifies spatial domains and marker genes across several biological systems. In SRT data from the brain, GASTON reveals gradients of neuronal differentiation and firing, and in SRT data from a tumor sample, GASTON infers gradients of metabolic activity and epithelial-mesenchymal transition (EMT)-related gene expression in the tumor microenvironment.
空间分辨转录组学技术可对组织切片中的基因表达进行高通量测量，但这些数据的稀疏性使得对空间基因表达模式（如基因表达梯度）的分析变得复杂。我们通过使用一种名为等深度值的新量来推导组织切片的等深度图（类似于地形图中的海拔图），从而解决这些问题。恒定等深度值的等高线包围了具有不同细胞类型组成的空间区域，而等深度值的梯度则指示了基因表达最大变化的空间方向。我们开发了GASTON，这是一种无监督且可解释的深度学习算法，它能同时学习等深度值、空间基因表达梯度以及等深度值的分段线性函数，这些函数可对单个基因表达中的连续梯度和不连续空间变化进行建模。我们通过证明GASTON能在多个生物系统中准确识别空间区域和标记基因来验证它。在来自大脑的空间分辨转录组学数据中，GASTON揭示了神经元分化和放电的梯度，而在来自肿瘤样本的空间分辨转录组学数据中，GASTON推断出肿瘤微环境中代谢活性和上皮-间质转化（EMT）相关基因表达的梯度。
