Computational approaches for the analyses of spatial profiling technologies

空间转录组学（ST）技术使地理基因表达 在肿瘤中进行分析。然而，每个肿瘤ST点包含不同的免疫和恶性细胞， 其细胞密度在组织区域之间显著变化。因此，细胞类型 肿瘤ST数据中的分解仍然是一个挑战，现有的用于大块肿瘤和 一般ST数据无法解析。我们开发了空间细胞估计（SpaCE）来推断细胞 身份和细胞间的相互作用从肿瘤ST数据。SpaCE首次估计癌症 通过模拟片段拷贝数变异的细胞克隆丰度。受约束 回归模型然后校准局部细胞密度并确定免疫和基质细胞 谱系分数。SpaCE提供了比现有方法更高的精度， 已知组成的样品和具有双盲病理学注释的人肿瘤 苏木精和伊红图像。此外，偶联细胞组分导致配体-受体 空间表达，SpaCE揭示了肿瘤-免疫界面的细胞间相互作用 促进癌症进展。

Spatial transcriptomics (ST) technology has enabled geographical gene expression profiling in tumors. However, each tumor ST spot contains diverse immune and malignant cells, with cell densities that vary significantly across tissue regions. Therefore, the cell type decomposition in tumor ST data remains a challenge that existing methods for bulk tumors and general ST data cannot resolve. We developed Spatial Cellular Estimator (SpaCE) to infer cell identities and intercellular interactions from tumor ST data. SpaCE first estimates cancer cell clonal abundance through modeling segmental copy number variations. A constrained regression model then calibrates local cell densities and determines immune and stromal cell lineage fractions. SpaCE provides higher accuracy than existing methods based on simulated samples of known composition and human tumors with double-blind pathology annotations on hematoxylin and eosin images. Moreover, coupling cell fraction results with ligand-receptor spatial expression, SpaCE reveals how intercellular interactions at the tumor-immune interface promote cancer progression.