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bioRxiv · 10.64898/2026.08.02.741948

MaskTalk: cell-identity-gated spatial lag for target-aware cell-cell communication inference in high-resolution spatial transcriptomics

Jia, P.·Chu, L.·Ren, Z.·Cui, H.·Shao, B.
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Abstract

MotivationHigh-resolution spatial transcriptomics enables single-cell ligand-receptor analysis, but unmasked receptor spatial lags include receptor expression from non-target neighbors, complicating the attribution of local communication signals to specified source-target cell-type pairs. ResultsWe present MaskTalk, a Python package implementing the cell-identity-gated spatial lag model (CIG-SLM). CIG-SLM restricts receptor-side neighborhoods to target cells through W(C) = WD(C). In cell-level breast cancer Visium HD data, CIG-SLM produced target-cell-dependent communication profiles relative to the matched LIANA+ bivariate unmasked baseline, and masked-specific records showed larger between-condition effect sizes and shorter physical source-target distances. Public breast cancer Xenium data demonstrated that MaskTalk runs on external cell-level spatial data and exhibits target-aware masking behavior. Availability and ImplementationImplemented in Python with AnnData; code is available at https://github.com/JiaPP1994/MaskTalk. Software and data archive DOIs are 10.5281/zenodo.21735883 and 10.5281/zenodo.21735951, respectively. Contactshaobin79@aliyun.com; cuihw2001423@163.com Supplementary InformationSupplementary Methods S1-S4, Figures S1-S3, and Tables S1-S5.

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Jia, P., Chu, L., Ren, Z., Cui, H., Shao, B.. 2026-08-06. MaskTalk: cell-identity-gated spatial lag for target-aware cell-cell communication inference in high-resolution spatial transcriptomics. https://doi.org/10.64898/2026.08.02.741948

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