Tumors are highly heterogeneous, consisting of cell populations with both transcriptional and genetic diversity. These diverse cell populations are spatially organized within a tumor, creating a distinct tumor microenvironment. A new technology called spatial transcriptomics can measure spatial patterns of gene expression within a tissue by sequencing RNA transcripts from a grid of spots, each containing a small number of cells. In tumor cells, these gene expression patterns represent the combined contribution of regulatory mechanisms, which alter the rate at which a gene is transcribed, and genetic diversity, particularly copy number aberrations (CNAs) which alter the number of copies of a gene in the genome. CNAs are common in tumors and often promote cancer growth through upregulation of oncogenes or downregulation of tumor-suppressor genes. We introduce a new method STARCH (spatial transcriptomics algorithm reconstructing copy-number heterogeneity) to infer CNAs from spatial transcriptomics data. STARCH overcomes challenges in inferring CNAs from RNA-sequencing data by leveraging the observation that cells located nearby in a tumor are likely to share similar CNAs. We find that STARCH outperforms existing methods for inferring CNAs from RNA-sequencing data without incorporating spatial information.

肿瘤具有高度异质性，由具有转录和遗传多样性的细胞群组成。这些不同的细胞群在肿瘤内进行空间组织，形成独特的肿瘤微环境。一种称为空间转录组学的新技术可以通过对点网格中的 RNA 转录本进行测序来测量组织内基因表达的空间模式，每个点包含少量细胞。在肿瘤细胞中，这些基因表达模式代表了调节机制（改变基因转录速率）和遗传多样性（尤其是改变基因组中基因拷贝数的拷贝数畸变 (CNA)）的综合作用. CNA 在肿瘤中很常见，通常通过上调致癌基因或下调肿瘤抑制基因来促进癌症生长。我们引入了一种新方法 STARCH（重建拷贝数异质性的空间转录组学算法）以从空间转录组学数据推断 CNA。STARCH 通过利用位于肿瘤附近的细胞可能共享相似 CNA 的观察，克服了从 RNA 测序数据推断 CNA 的挑战。我们发现 STARCH 在不包含空间信息的情况下从 RNA 测序数据推断 CNA 的方法优于现有方法。