Georgetown University - Translational Neurotherapeutics Program (TNP); Georgetown University - Department of Biostatistics, Bioinformatics and Biomathematics (DBBB)
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.
Wang, Zeya and Cao, Shaolong and Morris, Jeffrey and Ahn, Jaeil and Liu, Rongjie and Tyekucheva, Svitlana and Gao, Fan and Li, Bo and Lu, Wei and Tang, Ximing and Wistuba, Ignacio I. and Bowden, Michaela and Mucci, Lorelei and Loda, Massimo and Parmigiani, Giovanni and Holmes, Chris C. and Wang, Wenyi, Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration (2018). Available at SSRN: https://ssrn.com/abstract=3188487 or http://dx.doi.org/10.2139/ssrn.3188487
This version of the paper has not been formally peer reviewed.
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