Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration
Jeffrey S Morris,
Ignacio I. Wistuba,
Chris C Holmes,
Posted 08 Jun 2017
bioRxiv DOI: 10.1101/146795 (published DOI: 10.1016/j.isci.2018.10.028)
Posted 08 Jun 2017
Transcriptomic 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 towards linking tumor transcriptomic data with clinical outcomes. An R package, scripts and data are available: https://github.com/wwylab/DeMixT/.
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