期刊
ISCIENCE
卷 9, 期 -, 页码 451-+出版社
CELL PRESS
DOI: 10.1016/j.isci.2018.10.028
关键词
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资金
- U.S. National Cancer Institute [2R01 CA158113, R01CA174206, R01 CA183793, P30 CA016672]
- NIH [P30 CA016672, R01 CA178744, 1R01CA183793, R01 CA174206, 5R01 CA174206-05, RO1CA131945, R01CA187918, DoD PC130716, P50 CA90381]
- NSF [1550088]
- University of Texas Lung Specialized Programs of Research Excellence [P50CA70907]
- Prostate Cancer Foundation Challenge Award
- Prostate Cancer Foundation, United States
- MRC [MC_UP_A390_1107] Funding Source: UKRI
- Direct For Biological Sciences
- Div Of Biological Infrastructure [1550088] Funding Source: National Science Foundation
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: hftps://github.com/wwylab/DeMixTallmaterials.
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