It is demonstrated that DAHGT-CCI can more accurately reconstruct cell communication networks in complex tissue microenvironments, offering an indispensable computational tool for studying developmental processes, disease mechanisms, and potential therapeutic targets from a spatially resolved perspective.
Cell-cell communication through ligand-receptor (L-R) interactions orchestrates cardiac development, homeostasis, and disease progression, yet existing computational methods cannot infer directional signalling networks or distinguish causal from correlational interactions in spatial transcriptomics data. We present GRAIL-Heart, a graph-attention-based framework that integrates spatial tissue topology with multi-task learning to predict context-dependent L-R interactions, reconstruct gene expression, and infer causal signalling pathways. Validated on 42,654 cells across six cardiac regions from the Human Heart Cell Atlas, GRAIL-Heart achieves 94.3% AUROC for L-R prediction, successfully recovers known cardiac signalling pathways, and reveals region-specific complement system involvement in cardiac homeostasis. The method outperforms existing approaches by 56%, is uniquely capable of gene expression reconstruction (R2 = 0.996), and provides an interpretable, generalisable framework for prioritising high-confidence ligand-receptor hypotheses for downstream experimental validation across diverse tissues. Key innovations:• Spatial graph integration: Dual-edge architecture encoding both spatial proximity and ligand-receptor-specific relationships for context-aware interaction prediction.• Multi-task learning with causal inference: Simultaneous optimisation of L-R prediction, expression reconstruction, and inverse modelling to distinguish functionally important from correlational interactions.• Open-source implementation: Fully reproducible code, pretrained cardiac model, and interactive web explorer enabling broad adoption across spatial transcriptomics applications.
Tumo Kgabeng, Lulu Wang, H. Ngwangwa et al.· MethodsX· 0 citations
Prior-enhanced Inference for Spatial Transcriptomic Cell Type Mapping (PRISM), a novel three-stage framework integrating biological prior construction, pseudo-label generation, and multi-level ST refinement, shows strong robustness to domain shift and platform heterogeneity.
Yiheng Xu, Xuehao Wang, Shuqi Liu et al.· Bioinformatics· 0 citations
A Framework for Learning Over REgulatory-Embedding Networks (FloREN), a supervised and interpretable sample representation method that enables improved sample stratification and biomarker discovery and supports downstream analyses that found specific immune network mechanisms in immune-mediated inflammatory diseases (IMIDs).
Iñigo Clemente‐Larramendi, S. Hillion, D. Cornec et al.· bioRxiv· 0 citations
Spatial transcriptomics assays remain costly and technically demanding, restricting transcriptome-wide profiling to specialist settings and preventing routine clinical deployment. Predicting spatially resolved gene expression from H&E histology could close this gap, yet current methods largely ignore the underlying tissue architecture and rarely quantify how their predictions can be trusted. We introduce HierarchicalDAEW, a dual-graph architecture that addresses both gaps. On the spot graph, a Domain-Aware Edge-Weighted convolutional operator learns separate projections for inter-domain, intra-domain, and boundary edges derived from Leiden clustering, allowing the model to treat tissue heterogeneity as an explicit structural signal rather than an implicit one. A second gene-level graph then fuses protein-protein interaction priors from STRING-DB with tissue-specific co-expression through learned attention gating, propagating predictions from a landmark gene set to a broader gene panel. Reliability is handled through evidential uncertainty estimation, which produces far better calibrated confidence intervals than Monte Carlo dropout under identical conditions. Across six human Visium sections spanning breast, colorectal, prostate, and cerebellar tissue, and against thirteen published baselines, HierarchicalDAEW achieves the strongest correlation with ground-truth expression, with gains that hold up under multi-seed reproducibility checks and negative controls that rule out positional shortcuts. Ablations further confirm that both the domain-aware edge typing and the hierarchical depth are necessary to this improvement, and calibrated uncertainty estimates identify low-confidence predictions for pathologist review before clinical action.
Kritanu Chattopadhyay, Soumya Chatterjee, Ondrej Krejcar et al.· arXiv.org· 0 citations
Experiments demonstrate that the GraphFusionNN fusion strategy— integrating graph topology, spatial context, and external embeddings—significantly improves classification accuracy, macro-F1, and robustness compared to single-modality models.
Accurate inference of gene regulatory networks (GRNs) from single-cell gene expression data is challenging due to noise, data sparsity, and variability in gene-gene associations across cells. We propose CoReGRN (Contextual Refinement of Gene Regulatory Networks), a nonparametric, context-aware post-processing framework that refines inferred GRNs by reweighting candidate regulatory interactions using Mutual Information based association strength and local network context. The method uses empirical cumulative distribution function (ECDF) based scores to assess how unusual each interaction is relative to the connectivity patterns of the genes it connects. Then combines this contextual information with the original edge confidence scores. We evaluate the CoReGRN framework on gold-standard datasets from the BEELINE benchmark suite across multiple state-of-the-art GRN inference algorithms. The results show consistent performance improvements, with average absolute gains of 0.097 in AUROC, 0.130 in AUPR, 0.133 in MCC, and 0.095 in F1-Score. We further apply the framework to a single-cell HIV-Leishmaniasis dataset, where the refined networks support the analysis of disease-specific regulatory hubs and interactions. Comparison with existing biological knowledge identifies both known and potentially novel regulatory relationships across HIV infection, HIV-Leishmaniasis, and HIV-Visceral Leishmaniasis conditions. The analysis includes hub gene identification, interaction network analysis, and literature based validation using GeneMANIA.
E. M., Jereesh A S, G. S. Kumar· BMC Genomics· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
