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Decomprolute: A benchmarking platform designed for multiomics-based tumor deconvolution

View ORCID ProfileSong Feng, Anna Calinawan, Pietro Pugliese, Pei Wang, View ORCID ProfileMichele Ceccarelli, Francesca Petralia, View ORCID ProfileSara JC Gosline
doi: https://doi.org/10.1101/2023.01.05.522902
Song Feng
1Pacific Northwest National Laboratory, Seattle, WA
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Anna Calinawan
2Icahn School of Medicine at Mount Sinai School, New York, NY
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Pietro Pugliese
3University of Sannio, Benevento, Italy
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Pei Wang
2Icahn School of Medicine at Mount Sinai School, New York, NY
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Michele Ceccarelli
4University of Naples “Federico II”, Italy
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Francesca Petralia
2Icahn School of Medicine at Mount Sinai School, New York, NY
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Sara JC Gosline
1Pacific Northwest National Laboratory, Seattle, WA
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  • For correspondence: sara.gosline@pnnl.gov
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Abstract

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 Statement

The authors have declared no competing interest.

Footnotes

  • http://pnnl-compbio.github.io/decomprolute

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted January 06, 2023.
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Decomprolute: A benchmarking platform designed for multiomics-based tumor deconvolution
Song Feng, Anna Calinawan, Pietro Pugliese, Pei Wang, Michele Ceccarelli, Francesca Petralia, Sara JC Gosline
bioRxiv 2023.01.05.522902; doi: https://doi.org/10.1101/2023.01.05.522902
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Decomprolute: A benchmarking platform designed for multiomics-based tumor deconvolution
Song Feng, Anna Calinawan, Pietro Pugliese, Pei Wang, Michele Ceccarelli, Francesca Petralia, Sara JC Gosline
bioRxiv 2023.01.05.522902; doi: https://doi.org/10.1101/2023.01.05.522902

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