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bioRxiv · 10.64898/2026.08.12.744448

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

Zhang, Z.·Zheng, X.·Yuan, Q.·Luo, R.
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Abstract

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.

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Zhang, Z., Zheng, X., Yuan, Q., Luo, R.. 2026-08-18. Detection of Spatially Aberrant Cells in Spatial Transcriptomics Data by Conformal Prediction. https://doi.org/10.64898/2026.08.12.744448

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