Spatially-extended Flow Phixer (SpeF-Phixer): A Spatially Extended $\varphi$-Mixing Framework for Gene Regulatory Causal Inference in Spatial Gene Field
Toru Nagasaka, Takaaki Tachibana, Yukari Adachi, Hiroki Kagiyama, Ryota Ito, Mitsugu Fujita, Kimihiro Yamashita, Yoshihiro Kakeji

TL;DR
SpeF-Phixer is a novel framework that integrates spatial transcriptomics and histological images to infer directed gene regulatory triplets with spatial coherence, advancing causal inference in spatial gene fields.
Contribution
It introduces a spatially extended phi-mixing framework combining image-derived spatial data with gene expression to identify causal gene triplets in tissue sections.
Findings
Reduced candidate triplets by 3.6x10^4 to a consensus set
Significant directional bias in downstream edges consistent with databases
Higher coherence of inferred regulatory chains compared to null controls
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…
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Taxonomy
TopicsSingle-cell and spatial transcriptomics · Pluripotent Stem Cells Research · Cell Image Analysis Techniques
