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

Wang, Yucheng and Gorrie-Stone, Tyler J and Grant, Olivia A and Andrayas, Alexandria D and Zhai, Xiaojun and McDonald-Maier, Klaus D and Schalkwyk, Leonard C (2022) 'InterpolatedXY: a two-step strategy to normalise DNA methylation microarray data avoiding sex bias.' Bioinformatics, 38 (16). pp. 3950-3957. ISSN 1367-4803

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Abstract

Motivation Data 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 normalize sex chromosome data without introducing bias. Currently, existing methods do not provide unbiased solutions to normalize sex chromosome data, usually, they just process autosomal and sex chromosomes indiscriminately. Results Here, 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 normalization methods. By this new strategy, the autosomal CpGs are first normalized 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 neighbors on autosomes. The proposed two-step strategy can also be applied to other non-quantile-based normalization 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 normalization effect. Availability and implementation The 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.

Item Type: Article
Uncontrolled Keywords: Humans; Oligonucleotide Array Sequence Analysis; DNA Methylation; Protein Processing, Post-Translational; Female; Male; Sexism
Divisions: Faculty of Science and Health
Faculty of Social Sciences
Faculty of Science and Health > Life Sciences, School of
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Faculty of Social Sciences > Institute for Social and Economic Research
Faculty of Science and Health > Sport, Rehabilitation and Exercise Sciences, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 29 Jul 2022 14:20
Last Modified: 24 Nov 2022 20:38
URI: http://repository.essex.ac.uk/id/eprint/33086

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