arXiv:2512.08202v1 Announce Type: new
Abstract: Background and objective: Spatial transcriptomics provides rich spatial context but lacks sufficient resolution for large-scale causal inference. We developed SpeF-Phixer, a spatially extended phi-mixing framework integrating whole-slide image (WSI)-derived spatial cell distributions with mapped scRNA-seq expression fields to infer directed gene regulatory triplets with spatial coherence. Methods: Using CD103/CD8-immunostained colorectal cancer WSIs and publicly available scRNA-seq datasets, spatial gene fields were constructed around mapped cells and discretized for signed phi-mixing computation. Pairwise dependencies, directional signs, and triplet structures were evaluated through kNN-based neighborhood screening and bootstrap consensus inference. Mediation and convergence were distinguished using generalized additive models (GAMs), with spatial validity assessed by real-null comparisons and database-backed direction checks. Results: Across tissue patches, the pipeline reduced approximately 3.6×10^4 triplet candidates to a reproducible consensus set (approximately 3×10^2 per patch). The downstream edge (Y to Z) showed significant directional bias consistent with curated regulatory databases. Spatial path tracing demonstrated markedly higher coherence for real triplets than for null controls, indicating that inferred chains represent biologically instantiated regulatory flows. Conclusion: SpeF-Phixer extracts spatially coherent, directionally consistent gene regulatory triplets from histological images. This framework bridges single-cell molecular profiles with microenvironmental organization and provides a scalable foundation for constructing spatially informed causal gene networks.
CTIGuardian: A Few-Shot Framework for Mitigating Privacy Leakage in Fine-Tuned LLMs
arXiv:2512.12914v1 Announce Type: cross Abstract: Large Language Models (LLMs) are often fine-tuned to adapt their general-purpose knowledge to specific tasks and domains such as cyber