Publication

TOAST: improving reference-free cell composition estimation by cross-cell type differential analysis

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Last modified
  • 05/15/2025
Type of Material
Authors
    Ziyi Li, Emory UniversityHao Wu, Emory University
Language
  • English
Date
  • 2019-09-04
Publisher
  • BMC (part of Springer Nature)
Publication Version
Copyright Statement
  • © 2019 The Author(s).
License
Final Published Version (URL)
Title of Journal or Parent Work
ISSN
  • 1474-7596
Volume
  • 20
Issue
  • 1
Start Page
  • 190
End Page
  • 190
Grant/Funding Information
  • This project was partially supported by National Institutes of Health (R01GM122083 to HW and ZL, P01NS097206 and U01MH116441 to HW) and Emory University WHSC 2018 Synergy Award to HW.
Supplemental Material (URL)
Abstract
  • In the analysis of high-throughput data from complex samples, cell composition is an important factor that needs to be accounted for. Except for a limited number of tissues with known pure cell type profiles, a majority of genomics and epigenetics data relies on the "reference-free deconvolution" methods to estimate cell composition. We develop a novel computational method to improve reference-free deconvolution, which iteratively searches for cell type-specific features and performs composition estimation. Simulation studies and applications to six real datasets including both DNA methylation and gene expression data demonstrate favorable performance of the proposed method. TOAST is available at https://bioconductor.org/packages/TOAST.
Author Notes
Keywords
Research Categories
  • Biology, Genetics
  • Biology, Molecular
  • Biology, Microbiology

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