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Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

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 valida...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.isci.2018.10.028

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Publisher:
Elsevier Publisher's website
Journal:
iScience Journal website
Volume:
9
Pages:
451-460
Publication date:
2018-11-02
Acceptance date:
2018-10-27
DOI:
EISSN:
2589-0042
ISSN:
2589-0042
Pmid:
30469014
Source identifiers:
868977
Language:
English
Keywords:
Pubs id:
pubs:868977
UUID:
uuid:200eb33a-7306-4025-8693-25fb94fdf818
Local pid:
pubs:868977
Deposit date:
2019-04-04

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