Lulu Chen1, Chiung-Ting Wu1, Niya Wang2, David M Herrington3, Robert Clarke4, Yue Wang1. 1. Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA. 2. Search Ranking Unit, Google LLC, Mountain View, CA 94043, USA. 3. Department of Internal Medicine, Wake Forest University, Winston-Salem, NC 27157, USA. 4. Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA.
Abstract
SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: http://bioconductor.org/packages/debCAM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: http://bioconductor.org/packages/debCAM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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