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 modeling 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 tumor 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.

Knowledge graph reasoning (KGR) is a key technology that infers missing facts in knowledge graphs (KGs). Given that real-world scenarios typically encounter unseen KGs with new entities and new relations, researchers have begun to explore fully inductive KGR methods. This setting presents greater challenges and has not been fully explored. Current methods primarily construct relation graphs based on the original KG to facilitate message passing between relations. These models have made significant progress in achieving fully inductive reasoning. However, as relation graphs focus solely on the co-occurrence patterns between relations, they often fail to capture reasoning patterns in KGs, which causes the model to struggle in effectively distinguishing between different relations and entities. This limitation severely restrict the reasoning capabilities of existing methods. In light of this, we propose the Adaptive Rule Pattern Transfer model (AdaRPT) for KGR. It aims to leverage logical rules for each relation in the KG to learn more comprehensive and transferable knowledge representations for entities and relations. For entities, we design a non-parameter message passing model that aggregates path information from the query entity to other entities. The path information for each entity is then matched with rules to obtain the transferable feature of each entity. And for relations, we extract both reasoning and co-occurrence patterns from KGs as transferable relation features. Finally, a path-based graph neural network (GNN) is employed on the transferable features of entities and relations to perform reasoning on KGs. Extensive experimental evaluations on 43 datasets for both inductive and transductive reasoning demonstrate the effectiveness and generalization capability of AdaRPT.