Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

Zeya Wang; Shaolong Cao; Jeffrey S. Morris; Jaeil Ahn; Rongjie Liu; Svitlana Tyekucheva; Fan Gao; Bo Li; Wei Lu; Ximing Tang; Ignacio I. Wistuba; Michaela Bowden; Lorelei Mucci; Massimo Loda; Giovanni Parmigiani; Chris C. Holmes; Wenyi Wang

doi:10.1101/146795

Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

Zeya Wang, Shaolong Cao, Jeffrey S. Morris, Jaeil Ahn, Rongjie Liu, Svitlana Tyekucheva, Fan Gao, Bo Li, Wei Lu, Ximing Tang, Ignacio I. Wistuba, Michaela Bowden, Lorelei Mucci, Massimo Loda, Giovanni Parmigiani, Chris C. Holmes, Wenyi Wang

Research output: Contribution to journal › Article › peer-review

Abstract

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. An R package, scripts and data are available: https://github.com/wwylab/DeMixT/.

Original language	English (US)
Journal	Unknown Journal
DOIs	https://doi.org/10.1101/146795
State	Published - Jun 8 2017
Externally published	Yes

Keywords

computational tool
head and neck squamous cell carcinoma
RNA-seq data
statistical models
tumor heterogeneity
tumor-stroma-immune interaction

ASJC Scopus subject areas

Biochemistry, Genetics and Molecular Biology(all)
Agricultural and Biological Sciences(all)
Immunology and Microbiology(all)
Neuroscience(all)
Pharmacology, Toxicology and Pharmaceutics(all)

Access to Document

10.1101/146795

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Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. / Wang, Zeya; Cao, Shaolong; Morris, Jeffrey S.; Ahn, Jaeil; Liu, Rongjie; Tyekucheva, Svitlana; Gao, Fan; Li, Bo; Lu, Wei; Tang, Ximing; Wistuba, Ignacio I.; Bowden, Michaela; Mucci, Lorelei; Loda, Massimo; Parmigiani, Giovanni; Holmes, Chris C.; Wang, Wenyi.

In: Unknown Journal, 08.06.2017.

Research output: Contribution to journal › Article › peer-review

Wang, Zeya ; Cao, Shaolong ; Morris, Jeffrey S. ; Ahn, Jaeil ; Liu, Rongjie ; Tyekucheva, Svitlana ; Gao, Fan ; Li, Bo ; Lu, Wei ; Tang, Ximing ; Wistuba, Ignacio I. ; Bowden, Michaela ; Mucci, Lorelei ; Loda, Massimo ; Parmigiani, Giovanni ; Holmes, Chris C. ; Wang, Wenyi. / Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. In: Unknown Journal. 2017.

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abstract = "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. An R package, scripts and data are available: https://github.com/wwylab/DeMixT/.",

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AU - Loda, Massimo

AU - Parmigiani, Giovanni

AU - Holmes, Chris C.

AU - Wang, Wenyi

N1 - Publisher Copyright: The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

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