SPIDER: Spatially Integrated Denoising via Embedding Regularization with Single Cell Supervision

Motivation: Spatial transcriptomics (ST) technologies profile gene expression while preserving tissue architecture, enabling the study of spatial
cellular organization and microenvironmental interactions. However, raw ST data are heavily affected by technical noise, sparsity, and dropout
events, which obscure true biological signals and hinder downstream analyses. While recent denoising methods incorporate spatial neighborhood
information, they lack explicit supervision due to missing cell-type annotations in ST data. To address this challenge, we introduce SPIDER,
a semi-supervised framework that leverages independently generated, annotated single-cell RNA-seq (scRNA-seq) references to guide ST
denoising. SPIDER synthesizes pseudo-ST data from scRNA-seq to inject cell-type information without requiring paired measurements. The
method constructs three graphs capturing spatial proximity, transcriptional similarity in real-ST data, and transcriptional structure in pseudo-ST
data. Graph encoders map these representations into a shared latent space, and a domain-alignment module transfers biologically meaningful
structure from pseudo-ST to real-ST embeddings. A graph-attention decoder with a zero-inflated negative binomial objective reconstructs
denoised ST expression profiles.
Results: We benchmark SPIDER on human dorsolateral prefrontal cortex and breast cancer datasets. SPIDER consistently enhances spatial gene
expression patterns, recovers known tissue structures, and achieves superior clustering performance compared to existing approaches. Marker
gene analyses demonstrate improved spatial continuity and clearer anatomical organization. By directly producing denoised expression matrices,
SPIDER improves both accuracy and interpretability, providing a generalizable solution for robust ST data denoising.
Availability and Implementation: The source code is available at: https://github.com/compbiolabucf/SPIDER.