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Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration.

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.

SUBMITTER: Wang Z

PROVIDER: S-EPMC6249353 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration.

Wang Zeya Z Cao Shaolong S Morris Jeffrey S JS Ahn Jaeil J Liu Rongjie R Tyekucheva Svitlana S Gao Fan F Li Bo B Lu Wei W Tang Ximing X Wistuba Ignacio I II Bowden Michaela M Mucci Lorelei L Loda Massimo M Parmigiani Giovanni G Holmes Chris C CC Wang Wenyi W

iScience 20181102

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 validatio ...[more]

PMID: 30469014

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