De novo compartment deconvolution and weight estimation of tumor samples using DECODER

Xianlu Laura Peng, Richard A. Moffitt, Robert J. Torphy, Keith E. Volmar and Jen Jen Yeh ()
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Xianlu Laura Peng: University of North Carolina
Richard A. Moffitt: Stony Brook University
Robert J. Torphy: University of Colorado
Keith E. Volmar: University of North Carolina–Rex Healthcare
Jen Jen Yeh: University of North Carolina

Nature Communications, 2019, vol. 10, issue 1, 1-11

Abstract: Abstract Tumors are mixtures of different compartments. While global gene expression analysis profiles the average expression of all compartments in a sample, identifying the specific contribution of each compartment remains a challenge. With the increasing recognition of the importance of non-neoplastic components, the ability to breakdown the gene expression contribution of each is critical. Here, we develop DECODER, an integrated framework which performs de novo deconvolution and single-sample compartment weight estimation. We use DECODER to deconvolve 33 TCGA tumor RNA-seq data sets and show that it may be applied to other data types including ATAC-seq. We demonstrate that it can be utilized to reproducibly estimate cellular compartment weights in pancreatic cancer that are clinically meaningful. Application of DECODER across cancer types advances the capability of identifying cellular compartments in an unknown sample and may have implications for identifying the tumor of origin for cancers of unknown primary.

Date: 2019
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DOI: 10.1038/s41467-019-12517-7

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