MethylCC: Technology-independent estimation of cell type composition using differentially methylated regions

Stephanie C. Hicks, Rafael A. Irizarry

Research output: Contribution to journalArticle

Abstract

A major challenge in the analysis of DNA methylation (DNAm) data is variability introduced from intra-sample cellular heterogeneity, such as whole blood which is a convolution of DNAm profiles across a unique cell type. When this source of variability is confounded with an outcome of interest, if unaccounted for, false positives ensue. Current methods to estimate the cell type proportions in whole blood DNAm samples are only appropriate for one technology and lead to technology-specific biases if applied to data generated from other technologies. Here, we propose the technology-independent alternative: methylCC, which is available at https://github.com/stephaniehicks/methylCC.

Original languageEnglish (US)
Article number261
JournalGenome biology
Volume20
Issue number1
DOIs
StatePublished - Nov 29 2019

Fingerprint

methylation
DNA methylation
DNA Methylation
Technology
DNA
blood
cells
sampling
methodology

Keywords

  • Cell composition
  • DNA methylation
  • HumanMethylation27 BeadChip
  • HumanMethylation450 BeadChip
  • Microarray
  • Reduced representation bisulfite-sequencing
  • Whole blood
  • Whole genome bisulfite-sequencing

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

Cite this

MethylCC : Technology-independent estimation of cell type composition using differentially methylated regions. / Hicks, Stephanie C.; Irizarry, Rafael A.

In: Genome biology, Vol. 20, No. 1, 261, 29.11.2019.

Research output: Contribution to journalArticle

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