Evidence map›Paper›PMID 25212756›Full record

ArticleBioinformatics (Oxford, England)2015

UNDO: a Bioconductor R package for unsupervised deconvolution of mixed gene expressions in tumor samples.

Niya Wang, Ting Gong, Robert Clarke, Lulu Chen, Ie-Ming Shih, Zhen Zhang, Douglas A Levine, Jianhua Xuan, Yue Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed.

  1. Deep learning based deconvolution methods: A systematic review.Computational and structural biotechnology journal · 2025
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  8. MRI Radiogenomics in Precision Oncology: New Diagnosis and Treatment Method.Computational intelligence and neuroscience · 2022
    Review
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  14. Review
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  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Niya WangDepartment of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, Department of Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX 78957, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, Departments of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231 and Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA.
Ting GongDepartment of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, Department of Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX 78957, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, Departments of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231 and Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA.
Robert ClarkeDepartment of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, Department of Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX 78957, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, Departments of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231 and Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA.
Lulu ChenDepartment of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, Department of Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX 78957, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, Departments of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231 and Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA.
Ie-Ming ShihDepartment of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, Department of Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX 78957, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, Departments of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231 and Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA.
Zhen ZhangDepartment of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, Department of Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX 78957, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, Departments of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231 and Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA.
Douglas A LevineDepartment of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, Department of Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX 78957, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, Departments of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231 and Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA.
Jianhua XuanDepartment of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, Department of Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX 78957, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, Departments of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231 and Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA.
Yue WangDepartment of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, Department of Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Smithville, TX 78957, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, Departments of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231 and Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA.

Funding

Genomic and Proteomic Architecture of AtherosclerosisR01HL111362 · NHLBI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI HERRINGTON, DAVID MCLEOD · 2012 to 2021
$17.2M
Proteome Characterization Center: A Genoproteomics Pipeline for Cance ResearchU24CA160036 · NCI · JOHNS HOPKINS UNIVERSITY · PI CHAN, DANIEL WANYUI, ZHANG, HUI · 2011 to 2016
$10.7M
Novel Targets in Endocrine ResponsivenessU54CA149147 · NCI · GEORGETOWN UNIVERSITY · PI MADHAVAN, SUBHA · 2010 to 2014
$6.9M
Improved Diagnostic of the Muscular DystrophiesR01NS029525 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HOFFMAN, ERIC P. · 1991 to 2015
$6.5M
Collaborative Research in Integrative Cancer BiologyU01CA184902 · NCI · GEORGETOWN UNIVERSITY · PI CLARKE, ROBERT R., WANG, YUE · 2014 to 2018
$2.7M
NCI NIH HHS CA149147NCI NIH HHS CA160036NHLBI NIH HHS HL111362NINDS NIH HHS NS29525
6 · The paper itself

Abstract

summaryWe develop a novel unsupervised deconvolution method, within a well-grounded mathematical framework, to dissect mixed gene expressions in heterogeneous tumor samples. We implement an R package, UNsupervised DecOnvolution (UNDO), that can be used to automatically detect cell-specific marker genes (MGs) located on the scatter radii of mixed gene expressions, estimate cellular proportions in each sample and deconvolute mixed expressions into cell-specific expression profiles. We demonstrate the performance of UNDO over a wide range of tumor-stroma mixing proportions, validate UNDO on various biologically mixed benchmark gene expression datasets and further estimate tumor purity in TCGA/CPTAC datasets. The highly accurate deconvolution results obtained suggest not only the existence of cell-specific MGs but also UNDO's ability to detect them blindly and correctly. Although the principal application here involves microarray gene expressions, our methodology can be readily applied to other types of quantitative molecular profiling data. AVAILABILITY AND IMPLEMENTATION: UNDO is available at http://bioconductor.org/packages.

Indexed as

Gene Expression Regulation, NeoplasticSoftwareAlgorithmsBreast NeoplasmsComputer SimulationFemaleGene Expression ProfilingHigh-Throughput Nucleotide SequencingHumansSequence Analysis, RNATumor Cells, Cultured

Identifiers

PMID25212756
PMCPMC4271149

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.