ArticleCell reports methods2024
Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution.
Article in Cell reports methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- SCSEQ: A web tool for analyzing single-cell RNA-seq data.GigaScience · 2026Article
- Panorama: a database for the oncogenic evaluation of somatic mutations in pan-cancer.Database : the journal of biological databases and curation · 2026Article
- Considerations and Software for Successful Immune Cell Deconvolution Using Proteomics Data.Journal of proteome research · 2025Article
- Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data.eLife · 2024Article
- Article
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7 authors.
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Abstract
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.
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