Department of Medicine, Division of Infectious Diseases and Immunology
Bioinformatics | Biological Phenomena, Cell Phenomena, and Immunity | Cellular and Molecular Physiology | Computational Biology | Genomics
INTRODUCTION: Meta-analysis is a powerful means for leveraging the hundreds of experiments being run worldwide into more statistically powerful analyses. This is also true for the analysis of omic data, including genome-wide DNA methylation. In particular, thousands of DNA methylation profiles generated using the Illumina 450k are stored in the publicly accessible Gene Expression Omnibus (GEO) repository. Often, however, the intensity values produced by the BeadChip (raw data) are not deposited, therefore only pre-processed values -obtained after computational manipulation- are available. Pre-processing is possibly different among studies and may then affect meta-analysis by introducing non-biological sources of variability.
MATERIAL AND METHODS: To systematically investigate the effect of pre-processing on meta-analysis, we analysed four different collections of DNA methylation samples (datasets), each composed of two subsets, for which raw data from controls (i.e. healthy subjects) and cases (i.e. patients) are available. We pre-processed the data from each dataset with nine among the most common pipelines found in literature. Moreover, we evaluated the performance of regRCPqn, a modification of the RCP algorithm that aims to improve data consistency. For each combination of pre-processing (9 x 9), we first evaluated the between-sample variability among control subjects and, then, we identified genomic positions that are differentially methylated between cases and controls (differential analysis).
RESULTS AND CONCLUSION: The pre-processing of DNA methylation data affects both the between-sample variability and the loci identified as differentially methylated, and the effects of pre-processing are strongly dataset-dependent. By contrast, application of our renormalization algorithm regRCPqn: (i) reduces variability and (ii) increases agreement between meta-analysed datasets, both critical components of data harmonization.
DNA methylation, Metaanalysis, Methylation, Algorithms, Statistical data, Inflammatory bowel disease, Parkinson disease, Data reduction
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Copyright: © 2020 Sala et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
DOI of Published Version
Sala C, Di Lena P, Fernandes Durso D, Prodi A, Castellani G, Nardini C. Evaluation of pre-processing on the meta-analysis of DNA methylation data from the Illumina HumanMethylation450 BeadChip platform. PLoS One. 2020 Mar 10;15(3):e0229763. doi: 10.1371/journal.pone.0229763. PMID: 32155174; PMCID: PMC7064179. Link to article on publisher's site
Sala C, Di Lena P, Fernandes Durso D, Prodi A, Castellani G, Nardini C. (2020). Evaluation of pre-processing on the meta-analysis of DNA methylation data from the Illumina HumanMethylation450 BeadChip platform. Open Access Articles. https://doi.org/10.1371/journal.pone.0229763. Retrieved from https://escholarship.umassmed.edu/oapubs/4196
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