Publication

DeconPeaker, a Deconvolution Model to Identify Cell Types Based on Chromatin Accessibility in ATAC-Seq Data of Mixture Samples

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Last modified
  • 05/20/2025
Type of Material
Authors
    Huamei Li, Southeast UniversityAmit Sharma, University Hospital BonnKun Luo, Xinjiang Medical UniversityZhaohui Qin, Emory UniversityXiao Sun, Southeast UniversityHongde Liu, Southeast University
Language
  • English
Date
  • 2020-06-08
Publisher
  • Frontiers Media S.A.
Publication Version
Copyright Statement
  • © 2020 Li, Sharma, Luo, Qin, Sun and Liu.
License
Final Published Version (URL)
Title of Journal or Parent Work
Volume
  • 11
Start Page
  • 392
End Page
  • 392
Grant/Funding Information
  • This work was supported by the National Natural Science Foundation of China (Nos. 31371339, 81660471, 81830053, and 61972084).
Supplemental Material (URL)
Abstract
  • While our understanding of cellular and molecular processes has grown exponentially, issues related to the cell microenvironment and cellular heterogeneity have sparked a new debate concerning the cell identity. Cell composition (chromatin and nuclear architecture) poses a strong risk for dynamic changes in the diseased condition. Since chromatin accessibility patterns play a major role in human diseases, it is therefore anticipated that a deconvolution tool based on open chromatin data will provide better performance in identifying cell composition. Herein, we have designed the deconvolution tool “DeconPeaker,” which can precisely define the uniqueness among subpopulations of cells using open chromatin datasets. Using this tool, we simultaneously evaluated chromatin accessibility and gene expression datasets to estimate cell types and their respective proportions in a mixture of samples. In comparison to other known deconvolution methods, we observed the lowest average root-mean-square error (RMSE = 0.042) and the highest average correlation coefficient (r = 0.919) between the prediction and “true” proportion. As a proof-of-concept, we also tested chromatin accessibility data from acute myeloid leukemia (AML) and successfully obtained unique cell types associated with AML progression. Furthermore, we showed that chromatin accessibility represents more essential characteristics in the identification of cell types than gene expression. Taken together, DeconPeaker as a powerful tool has the potential to combine different datasets (primarily, chromatin accessibility and gene expression) and define different cell types in mixtures. The Python package of DeconPeaker is now available at https://github.com/lihuamei/DeconPeaker.
Author Notes
Keywords
Research Categories
  • Biology, Genetics
  • Biology, Cell
  • Engineering, Biomedical
  • Biology, Biostatistics

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