PT  - JOURNAL ARTICLE
AU  - Song Feng
AU  - Anna Calinawan
AU  - Pietro Pugliese
AU  - Pei Wang
AU  - Michele Ceccarelli
AU  - Francesca Petralia
AU  - Sara JC Gosline
TI  - Decomprolute: A benchmarking platform designed for multiomics-based tumor deconvolution
AID  - 10.1101/2023.01.05.522902
DP  - 2023 Jan 01
TA  - bioRxiv
PG  - 2023.01.05.522902
4099  - http://biorxiv.org/content/early/2023/01/06/2023.01.05.522902.short
4100  - http://biorxiv.org/content/early/2023/01/06/2023.01.05.522902.full
AB  - Tumor deconvolution is a reliable way to disentangle the 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-seq) rather than protein levels in tumor cells. While gene expression is less expensive to measure, protein levels provide a more accurate view of immune markers. To facilitate the development as well as improve the reproducibility and reusability of multi-omic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic data sets. Decomprolute incorporates the large-scale multiomic data sets 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. The platform consists of modular architecture and it comes with well-defined input and output formats at each module. As a result, it is robust and extendable easily with additional algorithms or analyses. The platform is available for access and use at http://pnnl-compbio.github.io/decomprolute.Motivation To provide a comprehensive platform for algorithm developers and researchers to benchmark and run tumor deconvolution algorithms on multiomic data.Competing Interest StatementThe authors have declared no competing interest.