Performance evaluation of dimensionality reduction techniques on high-dimensional DNA methylation data

DNA methylation (DNAm) is a key epigenetic modification, and datasets capturing DNAm are typically high-dimensional. Although dimension reduction (DR) techniques are commonly applied, it remains unclear how different DR methods perform specifically on the DNAm dataset. In this study, we aim to evaluate the performance of several DR techniques to determine their performance for reducing the dimensionality of DNAm datasets. We leveraged the DNAm dataset from the STRiDE (STratification of Risk of Diabetes in Early pregnancy) prospective study, which consists of 8,62,927 CpG sites each from 258 pregnant women. Women were categorized as Normal (<i>n</i> = 146); and GDM (<i>n</i> = 112). Epigenome-wide DNA methylation profiles from peripheral blood were quantified using an Infinium Methylation EPIC array. All stated DR techniques were performed, and the retained amount of information, local neighborhood preservation criteria, and global structure-holding approaches were assessed using various statistical measures and compared. Across Shannon entropy, local-neighborhood metrics (König's measure, Spearman's ρ, trustworthiness/continuity) and global-structure metrics (Kruskal stress, Sammon's stress, residual variance), MDS and PCA consistently achieved the best performance. PLS-DA trailed closely, while ISOMAP showed moderate results and UMAP performed worst, exhibiting higher entropy, lower correlation preservation, and greater distortion of both local and global structures.