Pacific Northwest National Laboratory, Seattle, WA, USA.
Icahn School of Medicine at Mount Sinai School, New York, NY, USA.
Cell Rep Methods. 2024 Feb 26;4(2):100708. doi: 10.1016/j.crmeth.2024.100708.
Tumor deconvolution enables the identification of diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold-standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA sequencing) rather than protein levels. Despite the popularity of gene expression datasets, protein levels often provide a more accurate view of rare cell types. To facilitate the use, development, and reproducibility of multiomic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic datasets. Decomprolute incorporates the large-scale multiomic datasets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. http://pnnl-compbio.github.io/decomprolute.
肿瘤去卷积能够识别构成实体瘤的多种细胞类型。然而，迄今为止，用于去卷积肿瘤样本的算法和用于评估算法的黄金标准数据集都偏向于分析基因表达（例如，RNA 测序），而不是蛋白质水平。尽管基因表达数据集很流行，但蛋白质水平通常提供了一种更准确的稀有细胞类型视图。为了促进多组学去卷积算法的使用、开发和可重复性，我们引入了 Decomprolute，这是一个 Common Workflow Language 框架，利用容器化来跨多组学数据集比较肿瘤去卷积算法。Decomprolute 结合了临床蛋白质组肿瘤分析联盟 (CPTAC) 生成的大规模多组学数据集，其中包括来自多种癌症类型的数千个肿瘤的匹配 mRNA 表达和蛋白质组数据，以构建完全开源的、容器化的蛋白质基因组肿瘤去卷积基准测试平台。http://pnnl-compbio.github.io/decomprolute。
