A key challenge of analyzing data from high-resolution spatial profiling technologies is to suitably represent the features of cellular neighborhoods or niches. Here we introduce the covariance environment (COVET), a representation that leverages the gene–gene covariate structure across cells in the niche to capture the multivariate nature of cellular interactions within it. We define a principled optimal transport-based distance metric between COVET niches that scales to millions of cells. Using COVET to encode spatial context, we developed environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA sequencing data into a latent space. ENVI includes two decoders: one to impute gene expression across the spatial modality and a second to project spatial information onto single-cell data. ENVI can confer spatial context to genomics data from single dissociated cells and outperforms alternatives for imputing gene expression on diverse spatial datasets.

分析高分辨率空间分析技术数据的一个关键挑战是适当地表示细胞邻域或生态位的特征。在这里，我们介绍了协方差环境（COVET），这是一种利用生态位中跨细胞的基因-基因协变量结构来捕获其中细胞相互作用的多变量性质的表示。我们定义了 COVET 生态位之间基于原则的最佳基于传输的距离度量，可扩展到数百万个单元。使用 COVET 对空间上下文进行编码，我们开发了环境变分推理 (ENVI)，这是一种条件变分自动编码器，可将空间和单细胞 RNA 测序数据联合嵌入到潜在空间中。 ENVI 包括两个解码器：一个用于估算整个空间模态的基因表达，第二个用于将空间信息投影到单细胞数据上。 ENVI 可以为来自单个分离细胞的基因组数据赋予空间背景，并且优于在不同空间数据集上估算基因表达的替代方案。