作者
Yucheng Wang, Tyler J Gorrie-Stone, Olivia A Grant, Alexandria D Andrayas, Xiaojun Zhai, Klaus D McDonald-Maier, Leonard C Schalkwyk
发表日期
2022/8/15
期刊
Bioinformatics
卷号
38
期号
16
页码范围
3950-3957
出版商
Oxford University Press
简介
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 …
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