NicheDeSig: Niche-aware Deconvolution and Adaptive Signature Analysis for Spatial Transcriptomics

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ID: 322974
2026
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Abstract
MOTIVATION: For spot-based spatial transcriptomics (ST), accurate cell-type deconvolution is essential for downstream analysis since each spot captures mixtures of multiple cell types. Meanwhile, spatial niches define distinct micro-environmental contexts, also salient for biological interpretation. However, existing deconvolution methods usually rely on fixed reference signatures or mapping single cells onto ST spots, without incorporating niche priors or modelling niche-dependent shifts. Consequently, existing methods remain focused on spot-level proportion estimation, with limited ability to support functional analysis of niche-associated molecular programs. RESULTS: We present NicheDeSig for niche-aware deconvolution. NicheDeSig models each cell type through adaptive signatures, enabling spot deconvolution under context-dependent signatures and supporting niche-aware analysis of cell-state variation across spatial micro-environments. Our method achieves strong deconvolution performance across the simulated benchmark datasets and improves spatial fidelity in the simulated colon dataset. The learned signatures recover laminar and white-matter-associated programs in the human dorsolateral prefrontal cortex (DLPFC), domain-stratified tumour microenvironment patterns in breast cancer (BRCA), and region-associated signatures in pancreatic ductal adenocarcinoma sample A (PDAC-A) and colorectal liver metastasis analyses. AVAILABILITY AND IMPLEMENTATION: Source code and the archived code snapshot are available at https://github.com/Davidcoach/NicheDeSig and https://doi.org/10.5281/zenodo.20685597.
Reference Key
openalex_W7171800970 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wen Xue, Juncheng Zhang, Tianyi Chen, Wenjun Shen, Jinjin Ma, Yong Xu, Hau−San Wong, Si Wu
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag578
URL
https://doi.org/10.1093/bioinformatics/btag578
Keywords Keywords not found

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