Detection of Spatially Aberrant Cells in Spatial Transcriptomics Data by Conformal Prediction

The hexagonal organization of epithelial cells represents a fundamental feature of normal tissue architecture, reflecting the precise spatial coordination that underlies healthy biological structure. Disruptions to this organization—manifesting as spatially aberrant spots with abnormal gene expression and misplaced positioning—are closely associated with disease initiation and progression. Here, we introduce SPADE, a computational framework that integrates single-cell RNA sequencing and spatial transcriptomics data to quantitatively characterize and detect spatial aberrancy. SPADE leverages a variational autoencoder coupled with Gaussian mixture modeling for cell-type embedding and spatial deconvolution, and incorporates conformal prediction to enable uncertainty-calibrated identification of aberrant spots. Through extensive validation, SPADE demonstrates superior performance in identifying biologically meaningful aberrant spots.