interpolatedXY: a two-step strategy to normalise DNA methylation microarray data avoiding sex bias

Abstract

AbstractMotivationData normalization is an essential step to reduce technical variation within and between arrays. Due to the different karyotypes and the effects of X chromosome inactivation, females and males exhibit distinct methylation patterns on sex chromosomes, thus it poses a significant challenge to normalise sex chromosome data without introducing bias. Currently, existing methods do not provide unbiased solutions to normalise sex chromosome data, usually, they just process autosomal and sex chromosomes indiscriminately.ResultsHere, we demonstrate that ignoring this sex difference will lead to introducing artificial sex bias, especially for thousands of autosomal CpGs. We present a novel two-step strategy (interpolatedXY) to address this issue, which is applicable to all quantile-based normalisation methods. By this new strategy, the autosomal CpGs are first normalised independently by conventional methods, such as funnorm or dasen; then the corrected methylation values of sex chromosome linked CpGs are estimated as the weighted average of their nearest neighbours on autosomes. The proposed two-step strategy can also be applied to other non-quantile-based normalisation methods, as well as other array-based data types. Moreover, we propose a useful concept: the sex explained fraction of variance, to quantitatively measure the normalisation effect.AvailabilityThe proposed methods are available by calling the function ‘adjustedDasen’ or ‘adjustedFunnorm’ in the latest wateRmelon package (https://github.com/schalkwyk/wateRmelon), with methods compatible with all the major workflows, including minfi.Contactxzhai@essex.ac.uk; lschal@essex.ac.ukSupplementary informationSupplementary data are available at...