The rapid development of spatial transcriptomics (ST) technologies has enabled transcriptome-wide profiling of gene expression in tissue sections. Despite the emergence of single-cell resolution platforms, most ST sequencing studies still operate at a multi-cell resolution. Consequently, deconvolution of cell identities within the spatial spots has become imperative for characterizing cell type-specific spatial organization. To this end, we developed SpatialDeX, a regression model-based method for estimating cell type proportions in tumor ST spots. SpatialDeX exhibited comparable performance to reference-based methods and outperformed other reference-free methods with simulated ST data. Using experimental ST data, SpatialDeX demonstrated superior performance compared with both reference-based and reference-free approaches. Additionally, a pan-cancer clustering analysis on tumor spots identified by SpatialDeX unveiled distinct tumor progression mechanisms both within and across diverse cancer types. Overall, SpatialDeX is a valuable tool for unraveling the spatial cellular organization of tissues from ST data without requiring scRNA-seq references.

中文翻译：

---

SpatialDeX 是一种用于实体瘤中空间转录组学数据细胞类型反卷积的无参考方法


空间转录组学 （ST） 技术的快速发展使组织切片中基因表达的转录组范围分析成为可能。尽管出现了单细胞分辨率平台，但大多数 ST 测序研究仍然以多细胞分辨率进行。因此，空间斑点内细胞身份的反卷积已成为表征细胞类型特异性空间组织的必要条件。为此，我们开发了 SpatialDeX，这是一种基于回归模型的方法，用于估计肿瘤 ST 点中的细胞类型比例。SpatialDeX 表现出与基于参考的方法相当的性能，并且在模拟 ST 数据方面优于其他无参考方法。使用实验 ST 数据，与基于参考和无参考的方法相比，SpatialDeX 表现出卓越的性能。此外，对 SpatialDeX 识别的肿瘤点的泛癌聚类分析揭示了不同癌症类型内部和之间的不同肿瘤进展机制。总体而言，SpatialDeX 是一种有价值的工具，无需 scRNA-seq 参考即可从 ST 数据中解开组织的空间细胞组织。