Wang Z., Cao S., Morris JS., Ahn J., Liu R., Tyekucheva S., Gao F., Li B., Lu W., Tang X., Wistuba II., Bowden M., Mucci L., Loda M., Parmigiani G., Holmes CC., Wang W.

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.

Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration.

Wang Z., Cao S., Morris JS., Ahn J., Liu R., Tyekucheva S., Gao F., Li B., Lu W., Tang X., Wistuba II., Bowden M., Mucci L., Loda M., Parmigiani G., Holmes CC., Wang W.

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