RT Journal Article
SR Electronic
T1 Decomprolute: A benchmarking platform designed for multiomics-based tumor deconvolution
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.01.05.522902
DO 10.1101/2023.01.05.522902
A1 Song Feng
A1 Anna Calinawan
A1 Pietro Pugliese
A1 Pei Wang
A1 Michele Ceccarelli
A1 Francesca Petralia
A1 Sara JC Gosline
YR 2023
UL http://biorxiv.org/content/early/2023/01/06/2023.01.05.522902.abstract
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.