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

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 meaningfu

SUBMITTER: Peng XL 

PROVIDER: S-EPMC6802116 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature