DeSpotX: Identifiability-Based Decontamination for Spatial Transcriptomics

Spatial transcriptomics (ST) at single-cell resolution profiles gene expression in its native spatial context, but a substantial fraction of transcripts contaminate neighboring cells, compromising downstream biological analyses. Existing decontamination methods either ignore the spatial structure of contamination or aggregate over neighbors without constraining how contamination is separated from native expression, leaving the decomposition ambiguous. To resolve this ambiguity, we introduce DeSpotX, a deep generative model that uses anchor genes, defined as genes not natively expressed in a given cell cluster, to constrain the contamination decomposition. DeSpotX further uses spatial information to estimate contamination locally through a cluster-masked, distance-weighted average over neighboring cells. On spike-in simulations across five datasets and four ST platforms, DeSpotX attains the highest AUROC in 27 of 30 benchmark settings, exceeding the best baseline by up to 0.12, and remains robust to inaccuracies in the cell-cluster annotation and in anchor gene construction. Using matched scRNA-seq as an independent reference for cell-type-specific expression, DeSpotX retains 94% of within-type signal while improving marker-gene specificity. Decontaminated expression also shows greater spatial coherence and yields cell-cell communication networks consistent with known biology. Finally, iterating between decontamination and cell-cluster annotation refines these outcomes, reassigning ligand-receptor signaling to expected source cells in mouse brain and breast cancer tissues.