DOI: 10.5061/dryad.cnp5hqc7v
title: A systematic evaluation of normalization methods and probe replicability using infinium EPIC methylation data

description: Background The Infinium EPIC array measures the methylation status
 of &gt; 850,000 CpG sites. The EPIC BeadChip uses a two-array design:
 Infinium Type I and Type II probes. These probe types exhibit different
 technical characteristics which may confound analyses. Numerous
 normalization and pre-processing methods have been developed to reduce
 probe type bias as well as other issues such as background and dye bias.
 Methods This study evaluates the performance of various normalization
 methods using 16 replicated samples and three metrics: absolute beta-value
 difference, overlap of non-replicated CpGs between replicate pairs, and
 effect on beta-value distributions. Additionally, we carried out Pearson’s
 correlation and intraclass correlation coefficient (ICC) analyses using
 both raw and SeSAMe 2 normalized data.  Results The method we
 define as SeSAMe 2, which consists of the application of the regular
 SeSAMe pipeline with an additional round of QC, pOOBAH masking, was found
 to be the best-performing normalization method, while quantile-based
 methods were found to be the worst performing methods. Whole-array
 Pearson’s correlations were found to be high. However, in agreement with
 previous studies, a substantial proportion of the probes on the EPIC array
 showed poor reproducibility (ICC &lt; 0.50). The majority of
 poor-performing probes have beta values close to either 0 or 1, and
 relatively low standard deviations. These results suggest that probe
 reliability is largely the result of limited biological variation rather
 than technical measurement variation. Importantly, normalizing the data
 with SeSAMe 2 dramatically improved ICC estimates, with the proportion of
 probes with ICC values &gt; 0.50 increasing from 45.18% (raw data) to
 61.35% (SeSAMe 2).
description: Study
 Participants and Samples  The whole blood samples were obtained from the Health, Well-being and Aging (Saúde, Ben-estar e Envelhecimento, SABE) study cohort. SABE is a cohort of census-withdrawn elderly from the city of São Paulo, Brazil, followed up every five years since the year 2000, with DNA first collected in 2010. Samples from 24 elderly adults were collected at two time points for a total of 48 samples. The first time point is the 2010 collection wave, performed from 2010 to 2012, and the second time point was set in 2020 in a COVID-19 monitoring project (9±0.71 years apart). The 24 individuals were 67.41±5.52 years of age (mean ± standard deviation) at time point one; and 76.41±6.17 at time point two and comprised 13 men and 11 women. All individuals enrolled in the SABE cohort provided written consent, and the ethic protocols were approved by local and national institutional review boards COEP/FSP/USP OF.COEP/23/10, CONEP 2044/2014, CEP HIAE 1263-10, University of Toronto RIS 39685.                                                     Blood Collection and Processing  Genomic DNA was extracted from whole peripheral blood samples collected in EDTA tubes. DNA extraction and purification followed manufacturer’s recommended protocols, using Qiagen AutoPure LS kit with Gentra automated extraction (first time point) or manual extraction (second time point), due to discontinuation of the equipment but using the same commercial reagents. DNA was quantified using Nanodrop spectrometer and diluted to 50ng/uL. To assess the reproducibility of the EPIC array, we also obtained technical replicates for 16 out of the 48 samples, for a total of 64 samples submitted for further analyses. Whole Genome Sequencing data is also available for the samples described above.  Characterization of DNA Methylation using the EPIC array  Approximately 1,000ng of human genomic DNA was used for bisulphite conversion. Methylation status was evaluated using the MethylationEPIC array at The Centre for Applied Genomics (TCAG, Hospital for Sick Children, Toronto, Ontario, Canada), following protocols recommended by Illumina (San Diego, California, USA).  Processing and Analysis of DNA Methylation Data   The R/Bioconductor packages <em>Meffil</em> (version 1.1.0), <em>RnBeads</em> (version 2.6.0), <em>minfi</em> (version 1.34.0) and <em>wateRmelon</em> (version 1.32.0) were used to import, process and perform quality control (QC) analyses on the methylation data. Starting with the 64 samples, we first used <em>Meffil</em> to infer the sex of the 64 samples and compared the inferred sex to reported sex. Utilizing the 59 SNP probes that are available as part of the EPIC array, we calculated concordance between the methylation intensities of the samples and the corresponding genotype calls extracted from their WGS data. We then performed comprehensive sample-level and probe-level QC using the <em>RnBeads</em> QC pipeline. Specifically, we (1) removed probes if their target sequences overlap with a SNP at any base, (2) removed known cross-reactive probes (3) used the iterative Greedycut algorithm to filter out samples and probes, using a detection p-value threshold of 0.01 and (4) removed probes if more than 5% of the samples having a missing value. Since <em>RnBeads</em> does not have a function to perform probe filtering based on bead number, we used the <em>wateRmelon</em> package to extract bead numbers from the IDAT files and calculated the proportion of samples with bead number &lt; 3. Probes with more than 5% of samples having low bead number (&lt; 3) were removed. For the comparison of normalization methods, we also computed detection p-values using out-of-band probes empirical distribution with the pOOBAH() function in the <em>SeSAMe</em> (version 1.14.2) R package, with a p-value threshold of 0.05, and the combine.neg parameter set to TRUE. In the scenario where pOOBAH filtering was carried out, it was done in parallel with the previously mentioned QC steps, and the resulting probes flagged in both analyses were combined and removed from the data.   Normalization Methods Evaluated   The normalization methods compared in this study were implemented using different R/Bioconductor packages and are summarized in Figure 1. All data was read into R workspace as RG Channel Sets using <em>minfi</em>’s read.metharray.exp() function. One sample that was flagged during QC was removed, and further normalization steps were carried out in the remaining set of 63 samples. Prior to all normalizations with <em>minfi</em>, probes that did not pass QC were removed. Noob, SWAN, Quantile, Funnorm and Illumina normalizations were implemented using <em>minfi</em>. BMIQ normalization was implemented with ChAMP (version 2.26.0), using as input Raw data produced by <em>minfi</em>’s preprocessRaw() function. In the combination of Noob with BMIQ (Noob+BMIQ), BMIQ normalization was carried out using as input <em>minfi</em>’s Noob normalized data. Noob normalization was also implemented with SeSAMe, using a nonlinear dye bias correction. For SeSAMe normalization, two scenarios were tested. For both, the inputs were unmasked SigDF Sets converted from <em>minfi</em>’s RG Channel Sets. In the first, which we call “SeSAMe 1”, SeSAMe’s pOOBAH masking was not executed, and the only probes filtered out of the dataset prior to normalization were the ones that did not pass QC in the previous analyses. In the second scenario, which we call “SeSAMe 2”, pOOBAH masking was carried out in the unfiltered dataset, and masked probes were removed. This removal was followed by further removal of probes that did not pass previous QC, and that had not been removed by pOOBAH. Therefore, SeSAMe 2 has two rounds of probe removal. Noob normalization with nonlinear dye bias correction was then carried out in the filtered dataset. Methods were then compared by subsetting the 16 replicated samples and evaluating the effects that the different normalization methods had in the absolute difference of beta values (|β|) between replicated samples.
description: We provide data on an Excel file, with absolute differences in
 beta values between replicate samples for each probe provided in different
 tabs for raw data and different normalization methods.