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- Title
De novo compartment deconvolution and weight estimation of tumor samples using DECODER.
- Authors
Peng, Xianlu Laura; Moffitt, Richard A.; Torphy, Robert J.; Volmar, Keith E.; Yeh, Jen Jen
- 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. Separating different cell compartments from bulk gene expression data can be challenging. Here the authors present DECODER, which can perform de novo deconvolutions on non-negative matrices including microarray, RNA-seq and ATAC-seq data sets.
- Subjects
DECONVOLUTION of absorption spectra; WEIGHT measurement; DECODERS (Electronics); GENE expression; RNA sequencing
- Publication
Nature Communications, 2019, Vol 10, Issue 1, pN.PAG
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-019-12517-7