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. DOI https://doi.org/10.1093/bioinformatics/btac436
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. DOI https://doi.org/10.1093/bioinformatics/btac436
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. DOI https://doi.org/10.1093/bioinformatics/btac436
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 |
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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 |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 29 Jul 2022 14:20 |
Last Modified: | 30 Oct 2024 16:32 |
URI: | http://repository.essex.ac.uk/id/eprint/33086 |
Available files
Filename: btac436.pdf
Licence: Creative Commons: Attribution 3.0