Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration - Wang, Z; Cao, S; Morris, J; Ahn, J; Liu, R; Tyekucheva, S; Gao, F; Li, B; Lu, W; Tang, X; Wistuba, I; Bowden, M; et al. (Elsevier, 2018)
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.