Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. Academic Article uri icon

Overview

abstract

  • Transcriptome deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We present DeMixT, a new tool to deconvolve high-dimensional data from mixtures of more than two components. DeMixT implements an iterated conditional mode algorithm and a novel gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application to TCGA data, DeMixT showed high accuracy. Improved deconvolution is an important step toward linking tumor transcriptomic data with clinical outcomes. An R package, scripts, and data are available: https://github.com/wwylab/DeMixTallmaterials.

publication date

  • November 2, 2018

Identity

PubMed Central ID

  • PMC6249353

Scopus Document Identifier

  • 85065297851

Digital Object Identifier (DOI)

  • 10.1016/j.isci.2018.10.028

PubMed ID

  • 30469014

Additional Document Info

volume

  • 9