Evaluation of pre-processing on the meta-analysis of DNA methylation data from the Illumina HumanMethylation450 BeadChip platform

• Published in: PLOS ONE Vol. 15; no. 3; p. e0229763
• Main Authors: Sala, Claudia, Di Lena, Pietro, Fernandes Durso, Danielle, Prodi, Andrea, Castellani, Gastone, Nardini, Christine
• Format: Journal Article Publication
• Language: English
• Published: United States Public Library of Science (PLoS) 10.03.2020; Public Library of Science
• Subjects: Algorithms; Animals; Biochemistry; Bioinformatics; Biology and life sciences; Biostatistics; Computational Biology; Computer and Information Sciences; Computer applications; Critical components; Data reduction; Datasets; Deoxyribonucleic acid; Disease; DNA; DNA methylation; Gene expression; Genetic research; Genomes; Genomics; High-Throughput Nucleotide Sequencing; Humans; Inflammatory bowel disease; Laboratories; Medicine; Medicine and Health Sciences; Meta-analysis; Meta-Analysis as Topic; Meta-analysis, DNA methylation, Illumina HumanMethylation450 BeadChip, preprocessing, normalization; Metaanalysis; Methods; Methylation; methylaton pre-processing; Parkinson disease; Parkinson's disease; Performance evaluation; Physical Sciences; Pipelines; Q; R; Research and Analysis Methods; Research Article; Science; Sequence Analysis, DNA; Software; Statistical analysis; Statistical data; Statistical methods; Variability; Italy
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0229763

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. 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 × 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). 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.