SpatioCAD：面向异质组织的细胞密度感知图扩散模型识别空间可变基因
SpatioCAD: Context-aware graph diffusion model for pinpointing spatially variable genes in heterogeneous tissues
SpatioCAD 是一个用于识别空间可变基因（SVG）的计算框架，通过显式解耦真实空间表达模式与细胞密度差异带来的混杂效应，降低异质组织中的假阳性。该方法引入图扩散模型，在模拟表达传播时纳入细胞密度感知条件，从而支持在较宽表达水平范围内识别 SVG。在模拟数据上的系统评测显示其统计功效与特异性更优；应用于乳腺癌、肺癌和胶质瘤数据集时，识别出功能多样的 SVG，包括与肿瘤进展相关的低丰度转录本，并重现了有生物学意义的组织结构特征。作者指出该方法速度快、可扩展到大组织。
来源：PLOS Computational Biology · journals.plos.org
by Hao Wen, Qunlun Shen, Shuqin Zhang
Spatial transcriptomics enables comprehensive characterization of tissue architecture, and the identification of spatially variable genes (SVGs) is a critical step for defining region-specific molecular markers and uncovering spatially regulated mechanisms across diverse biological contexts. However, most existing methods for SVG detection overlook cell density variations, a major confounding factor in complex tissues such as tumors, where heterogeneous cellularity frequently introduces false-positive calls. Here we present SpatioCAD, a computational framework that explicitly decouples genuine spatial expression patterns from confounding effects driven by cellularity. SpatioCAD introduces a graph diffusion model that inherently incorporates cell-density-aware conditions to simulate expression propagation, supporting the identification of SVGs across a broad range of expression levels. Systematic evaluations on simulated datasets demonstrate its superior statistical power and specificity. Applied to breast cancer, lung cancer, and glioma datasets, SpatioCAD identifies functionally diverse SVGs, including low‑abundance transcripts with established roles in tumor progression, while also recapitulates biologically meaningful tissue architecture features. Importantly, SpatioCAD is fast and scalable to large tissues.