Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution.
Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution.
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Decomprute 是一个基准测试平台，专为基于多组学的肿瘤反卷积而设计。
DOI：
10.1016/j.crmeth.2024.100708
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Gosline,SaraJC
中科院分区：
文献类型：
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作者：
Feng,Song;Calinawan,Anna;Pugliese,Pietro;Wang,Pei;Ceccarelli,Michele;Petralia,Francesca;Gosline,SaraJC
肿瘤去卷积能够识别构成实体瘤的不同细胞类型。然而，到目前为止，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是针对基因表达(例如，RNA测序)而不是蛋白质水平的分析。尽管基因表达数据集很受欢迎，但蛋白质水平往往提供了对罕见细胞类型的更准确的看法。为了促进多组体去卷积算法的使用、开发和重复性，我们引入了Decomprolute，这是一个通用的工作流语言框架，它利用集装化来比较多组体数据集中的肿瘤去卷积算法。Decomprolute整合了临床蛋白质组肿瘤分析联盟(CPTAC)产生的大规模多组数据集，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，以构建一个完全开源、容器化的蛋白质组肿瘤反卷积基准平台。Http://pnnl-compbio.github.io/decomprolute
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