A Systematic Assessment of Cell Type Deconvolution Algorithms for DNA Methylation Data

Details

• Personal Author:
  Kuan P-F ; Song J
• Description:
  We performed systematic assessment of computational deconvolution methods that play an important role in the estimation of cell type proportions from bulk methylation data. The proposed framework methylDeConv (available as an R package) integrates several deconvolution methods for methylation profiles (Illumina HumanMethylation450 and MethylationEPIC arrays) and offers different cell-type-specific CpG selection to construct the extended reference library which incorporates the main immune cell subsets, epithelial cells and cell-free DNAs. We compared the performance of different deconvolution algorithms via simulations and benchmark datasets and further investigated the associations of the estimated cell type proportions to cancer therapy in breast cancer and subtypes in melanoma methylation case studies. Our results indicated that the deconvolution based on the extended reference library is critical to obtain accurate estimates of cell proportions in non-blood tissues. [Description provided by NIOSH]
• Subjects:
  Biochemistry Occupational Health Safety
• Keywords:
  Algorithms Analytical Processes Author Keywords: DNA Methylation Biochemical Analysis Bioinformatics Cell Type Cell Type Heterogeneity Deconvolution DNA Damage Epigenetics EWAS Genomics Models

  More +
• ISSN:
  1477-4054
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  New York ; OSHA Region 2
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  23
• Issue:
  6
• NIOSHTIC Number:
  nn:20066189
• Citation:
  Brief Bioinform 2022 Nov; 23(6):bbac449
• Contact Point Address:
  Pei-Fen Kuan, Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Nicolls Road, Math Tower, Room 1-106, Stony Brook, NY 11794, USA
• Email:
  peifen.kuan@stonybrook.edu
• Federal Fiscal Year:
  2023
• Performing Organization:
  State University of New York at Stony Brook
• Peer Reviewed:
  True
• Start Date:
  20170701
• Source Full Name:
  Briefings in Bioinformatics
• End Date:
  20200630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:cc64d2d15b8075d443180c7c79bd05fb4e7c26e667afe7e91b87dcc24d2fdc756921ff20022bad4678c5080570a4fe285f58ec09972e6491251a84d9113b0e72
• Download URL:
  https://stacks.cdc.gov/view/cdc/212686/cdc_212686_DS1.pdf
• File Type:
  [PDF - 1.87 MB ]