BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference

Genome Biology
Elior RahmaniEran Halperin

Abstract

We introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell-type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer cell counts without methylation reference only capture linear combinations of cell counts rather than provide one component per cell type. Our approach allows the construction of components such that each component corresponds to a single cell type, and provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before.

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Citations

Nov 13, 2019·Epigenetics & Chromatin·Liduo YinHehuang Xie
Aug 15, 2020·Current Environmental Health Reports·Kyle A CampbellKelly M Bakulski
Jul 17, 2019·Nature Communications·Xiangyu LuoYingying Wei
Jan 6, 2021·Briefings in Bioinformatics·Ziyi Chen, Aiping Wu
Jul 23, 2020·Computational and Structural Biotechnology Journal·Fei FanKun Sun
Jan 21, 2021·Reproductive Sciences·Jagyashila Das, Arindam Maitra
Oct 24, 2020·Briefings in Bioinformatics·Mingguang ShiHao Tang
May 13, 2021·Nature Communications·Christa CaggianoNoah Zaitlen

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Datasets Mentioned

BETA
GSE40279
GSE42861
GSE80417
GSE84727

Methods Mentioned

BETA
blood draw
PCA

Software Mentioned

RefFreeEWAS R package
NNMF
fastfit
Clarity
minfi R package
IDAT
R
MeDeCom R package
GLINT
BayesCCE

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