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 journalArticlepeer-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 languageEnglish (US)
Pages (from-to)3927-3929
Number of pages3
JournalBioinformatics
Volume36
Issue number12
DOIs
StatePublished - Mar 31 2020
Externally publishedYes

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.

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

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