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:10.1016/j.isci.2018.10.028

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

47 Scopus citations

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)
Pages (from-to)	451-460
Number of pages	10
Journal	iScience
Volume	9
DOIs	https://doi.org/10.1016/j.isci.2018.10.028
State	Published - Nov 30 2018

Keywords

Cancer
Computational Bioinformatics
Transcriptomics

ASJC Scopus subject areas

General

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10.1016/j.isci.2018.10.028

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@article{e249b2135be0475d82cf58f09d9f4bb9,

title = "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 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.",

keywords = "Cancer, Computational Bioinformatics, Transcriptomics",

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

note = "Publisher Copyright: {\textcopyright} 2018",

year = "2018",

month = nov,

day = "30",

doi = "10.1016/j.isci.2018.10.028",

language = "English (US)",

volume = "9",

pages = "451--460",

journal = "iScience",

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AU - Wang, Zeya

AU - Cao, Shaolong

AU - Morris, Jeffrey S.

AU - Ahn, Jaeil

AU - Liu, Rongjie

AU - Tyekucheva, Svitlana

AU - Gao, Fan

AU - Li, Bo

AU - Lu, Wei

AU - Tang, Ximing

AU - Wistuba, Ignacio I.

AU - Bowden, Michaela

AU - Mucci, Lorelei

AU - Loda, Massimo

AU - Parmigiani, Giovanni

AU - Holmes, Chris C.

AU - Wang, Wenyi

PY - 2018/11/30

Y1 - 2018/11/30

N2 - 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.

AB - 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.

KW - Cancer

KW - Computational Bioinformatics

KW - Transcriptomics

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DO - 10.1016/j.isci.2018.10.028

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AN - SCOPUS:85065297851

SN - 2589-0042

VL - 9

SP - 451

EP - 460

JO - iScience

JF - iScience

ER -

Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

Abstract

Keywords

ASJC Scopus subject areas

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