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.
Original language | English (US) |
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Pages (from-to) | 451-460 |
Number of pages | 10 |
Journal | iScience |
Volume | 9 |
DOIs | |
State | Published - Nov 30 2018 |
Keywords
- Cancer
- Computational Bioinformatics
- Transcriptomics
ASJC Scopus subject areas
- General