DebCAM: A bioconductor R package for fully unsupervised deconvolution of complex tissues

Lulu Chen; Chiung Ting Wu; Niya Wang; David M. Herrington; Robert Clarke; Yue Wang

doi:10.1093/bioinformatics/btaa205

DebCAM: A bioconductor R package for fully unsupervised deconvolution of complex tissues

Lulu Chen, Chiung Ting Wu, Niya Wang, David M. Herrington, Robert Clarke, Yue Wang

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

1 Scopus citations

Abstract

We develop a fully unsupervised deconvolution method to dissect complex tissues into molecularly distinctive tissue or cell subtypes based on bulk expression profiles. We implement an R package, deconvolution by Convex Analysis of Mixtures (debCAM) that can automatically detect tissue/cell-specific markers, determine the number of constituent subtypes, calculate subtype proportions in individual samples and estimate tissue/cell-specific expression profiles. We demonstrate the performance and biomedical utility of debCAM on gene expression, methylation, proteomics and imaging data. With enhanced data preprocessing and prior knowledge incorporation, debCAM software tool will allow biologists to perform a more comprehensive and unbiased characterization of tissue remodeling in many biomedical contexts.

Original language	English (US)
Pages (from-to)	3927-3929
Number of pages	3
Journal	Bioinformatics
Volume	36
Issue number	12
DOIs	https://doi.org/10.1093/bioinformatics/btaa205
State	Published - Mar 31 2020
Externally published	Yes

Bibliographical note

Funding Information:
This work was supported by the National Institutes of Health [HL111362-05A1, HL133932, NS115658]; and the Department of Defence [W81XWH-18-1-0723, BC171885P1].

Publisher Copyright:
© 2020 The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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Journal Article
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title = "DebCAM: A bioconductor R package for fully unsupervised deconvolution of complex tissues",

abstract = " We develop a fully unsupervised deconvolution method to dissect complex tissues into molecularly distinctive tissue or cell subtypes based on bulk expression profiles. We implement an R package, deconvolution by Convex Analysis of Mixtures (debCAM) that can automatically detect tissue/cell-specific markers, determine the number of constituent subtypes, calculate subtype proportions in individual samples and estimate tissue/cell-specific expression profiles. We demonstrate the performance and biomedical utility of debCAM on gene expression, methylation, proteomics and imaging data. With enhanced data preprocessing and prior knowledge incorporation, debCAM software tool will allow biologists to perform a more comprehensive and unbiased characterization of tissue remodeling in many biomedical contexts. ",

author = "Lulu Chen and Wu, {Chiung Ting} and Niya Wang and Herrington, {David M.} and Robert Clarke and Yue Wang",

note = "Funding Information: This work was supported by the National Institutes of Health [HL111362-05A1, HL133932, NS115658]; and the Department of Defence [W81XWH-18-1-0723, BC171885P1]. Publisher Copyright: {\textcopyright} 2020 The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.",

year = "2020",

month = mar,

day = "31",

doi = "10.1093/bioinformatics/btaa205",

language = "English (US)",

volume = "36",

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journal = "Bioinformatics",

issn = "1367-4803",

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AU - Chen, Lulu

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AU - Wang, Niya

AU - Herrington, David M.

AU - Clarke, Robert

AU - Wang, Yue

N1 - Funding Information: This work was supported by the National Institutes of Health [HL111362-05A1, HL133932, NS115658]; and the Department of Defence [W81XWH-18-1-0723, BC171885P1]. Publisher Copyright: © 2020 The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

PY - 2020/3/31

Y1 - 2020/3/31

N2 - We develop a fully unsupervised deconvolution method to dissect complex tissues into molecularly distinctive tissue or cell subtypes based on bulk expression profiles. We implement an R package, deconvolution by Convex Analysis of Mixtures (debCAM) that can automatically detect tissue/cell-specific markers, determine the number of constituent subtypes, calculate subtype proportions in individual samples and estimate tissue/cell-specific expression profiles. We demonstrate the performance and biomedical utility of debCAM on gene expression, methylation, proteomics and imaging data. With enhanced data preprocessing and prior knowledge incorporation, debCAM software tool will allow biologists to perform a more comprehensive and unbiased characterization of tissue remodeling in many biomedical contexts.

AB - We develop a fully unsupervised deconvolution method to dissect complex tissues into molecularly distinctive tissue or cell subtypes based on bulk expression profiles. We implement an R package, deconvolution by Convex Analysis of Mixtures (debCAM) that can automatically detect tissue/cell-specific markers, determine the number of constituent subtypes, calculate subtype proportions in individual samples and estimate tissue/cell-specific expression profiles. We demonstrate the performance and biomedical utility of debCAM on gene expression, methylation, proteomics and imaging data. With enhanced data preprocessing and prior knowledge incorporation, debCAM software tool will allow biologists to perform a more comprehensive and unbiased characterization of tissue remodeling in many biomedical contexts.

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JO - Bioinformatics

JF - Bioinformatics

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ER -

DebCAM: A bioconductor R package for fully unsupervised deconvolution of complex tissues

Abstract

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