Authors
Lulu Chen, Chiung-Ting Wu, Niya Wang, David M Herrington, Robert Clarke, Yue Wang
Published in
Bioinformatics (Oxford, England).
Volume 36.
Issue 12.
Pages 3927-3929.
Jun 01, 2020.
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.
bioconductor.org/packages/debCAM.
Supplementary data are available at Bioinformatics online.
PMID:
32219387
Bibliographic data and abstract were imported from PubMed on Nov 01, 2021.
Please sign in to see all details.
Did you like this publication? Sign up with Life Science Network.
If you already have a Life Science Network, LinkedIn or Google account:
Read more here: Source link