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DeSpotX: Identifiability-Based Decontamination for Spatial Transcriptomics.

Ruo Han Wang¹, Andrew J Gentles¹

¹Stanford University.

Biorxiv : the Preprint Server for Biology

|May 25, 2026

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DeSpotX, a new deep learning model, accurately removes RNA contamination in spatial transcriptomics (ST) data. This improves gene expression analysis and biological insights from ST datasets.

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Spatial transcriptomics (ST) offers gene expression profiling in native tissue context.
Transcript contamination between adjacent cells in ST data compromises downstream analyses.
Current decontamination methods lack spatial awareness and can be ambiguous.

Purpose of the Study:

To develop a novel computational method for accurate transcript decontamination in spatial transcriptomics data.
To improve the biological interpretability of spatial transcriptomics by resolving ambiguous contamination.
To enhance marker-gene specificity and cell-cell communication network inference.

Main Methods:

Introduced DeSpotX, a deep generative model utilizing anchor genes to constrain contamination decomposition.

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Employed spatial information for local contamination estimation via a cluster-masked, distance-weighted average.

Incorporated a learned diffusion prior to prevent over-correction of low-expression signals.

Main Results:

DeSpotX achieved AUROC > 0.94 across five simulated datasets and four ST platforms, outperforming baselines by 0.02-0.12.
Demonstrated robustness to inaccuracies in cell-cluster annotation and anchor gene identification.
Validated improved marker-gene specificity, spatial coherence, and biologically relevant cell-cell communication networks on real tissues.

Conclusions:

DeSpotX effectively resolves contamination ambiguity in spatial transcriptomics data.
The method enhances the accuracy and biological relevance of spatial transcriptomics analyses.
Iterative refinement of decontamination and cell-cluster annotation further improves biological insights, such as ligand-receptor signaling localization.