recountmethylation enables flexible analysis of public blood DNA methylation array data

Abstract

Thousands of DNA methylation (DNAm) array samples from human blood are publicly available on the Gene Expression Omnibus (GEO), but they remain underutilized for experiment planning, replication, and cross-study and cross-platform analyses. To facilitate these tasks, we augmented our recountmethylation R/Bioconductor package with 12,537 uniformly processed EPIC and HM450K blood samples on GEO as well as several new features. We subsequently used our updated package in several illustrative analyses, finding (1) study ID bias adjustment increased variation explained by biological and demographic variables, (2) most variation in autosomal DNAm was explained by genetic ancestry and CD4+ T-cell fractions, and (3) the dependence of power to detect differential methylation on sample size was similar for each of peripheral blood mononuclear cells (PBMC), whole blood, and umbilical cord blood. Finally, we used PBMC and whole blood to perform independent validations, and we recovered 40-46% of differentially methylated probes (DMPs) between sexes from two previously published epigenome-wide association studies (EWAS).

recountmethylationenables flexible analysis of public blood DNA methylation array data

“…We downloaded raw intensity data (IDATs) and metadata (SOFTs; top right), processed GEO metadata (middle) and DNAm signals (bottom right) into HDF5-based data formats (bottom middle), and finally updated our server and the recountmethylation Bioconductor package (bottom left). Color outlines indicate data access and processing using tools we developed [green= ( Maden et al , 2021a ), blue= ( Maden et al , 2021b ), green= ( Maden et al , 2022 )]. Diagrams were created with…”

“…These features are explained in package vignettes. Further, a new Snakemake workflow available on GitHub ( Maden et al , 2022 ) allows users to create their own compilations of public DNAm array data on GEO ( Mölder et al , 2021 ), with the functionality to customize output data types and attributes predicted from GEO metadata. As shown below, our resources enable identification of biomarker candidates, independent validation and replication of previous research, experiment planning and more.…”

“…We analyzed DNAm array data from the three most prevalent blood sample types in the GEO database and updated the Bioconductor package to make reproducible ( Beaulieu-Jones and Greene, 2017 ; Heil et al , 2021 ) cross-study and cross-platform analyses of these data easier. Since HM450K and EPIC data continue to accumulate rapidly on GEO, we further developed the Snakemake workflow to enable semi-automated compilation of the DNAm array data on GEO ( Maden et al , 2022 ).…”

“…DNAm array data were identified, downloaded and processed using the v0.0.1 ( Maden et al , 2022 ) Snakemake workflow. It comprises the v1.0.0 ( Maden et al , 2021a ) and v1.0.0 ( Maden et al , 2021b ) tools, which we used previously to compile HM450K data ( Maden et al , 2021c ).…”

“…The Snakemake workflow is available on GitHub ( Maden et al , 2022 ). This will be useful for researchers hoping to make and update new compilations of public DNAm array data from GEO.…”