Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

Zeya Wang, Shaolong Cao, Jeffrey S. Morris, Jaeil Ahn, Rongjie Liu, Svitlana Tyekucheva, Fan Gao, Bo Li, Wei Lu, Ximing Tang, Ignacio I. Wistuba, Michaela Bowden, Lorelei Mucci, Massimo Loda, Giovanni Parmigiani, Chris C. Holmes, Wenyi Wang

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


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:

Original languageEnglish
Pages (from-to)451-460
Number of pages10
StatePublished - Nov 30 2018

Bibliographical note

Publisher Copyright:
© 2018


  • Cancer
  • Computational Bioinformatics
  • Transcriptomics

ASJC Scopus subject areas

  • General


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