MaskTalk: cell-identity-gated spatial lag for target-aware cell-cell communication inference in high-resolution spatial transcriptomics
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Motivation
High-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.
Results
We 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 Implementation
Implemented 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.
Contact
shaobin79@aliyun.com ; cuihw2001423@163.com
Supplementary Information
Supplementary Methods S1-S4, Figures S1-S3, and Tables S1-S5.
