The covariance environment defines cellular niches for spatial inference

作者：Doron Haviv, Ján Remšík, Mohamed I. Gatie, Catherine Snopkowski, Meril Takizawa, Nathan Pereira, John Bashkin, Stevan Jovanovich, Tal Nawy, Ronan Chaligné, Adrienne Boire, Anna‐Katerina Hadjantonakis, Dana Pe’er · 发表于：Nature Biotechnology · 年份：2024 · DOI：10.1038/s41587-024-02193-4 · 被引用次数：63 · 研究领域：Single-cell and spatial transcriptomics、Cell Image Analysis Techniques、Gene expression and cancer classification

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.