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

Area of Science:

• Genomics
• Epigenetics
• Bioinformatics

Background:

• Data normalization is crucial for reducing technical variation in array-based studies.
• Sex chromosomes (X and Y) have distinct methylation patterns due to karyotype differences and X-chromosome inactivation, complicating normalization.
• Existing normalization methods often fail to account for sex-specific methylation, introducing artificial bias.

Purpose of the Study:

• To address the challenge of unbiased normalization of sex chromosome data in methylation arrays.
• To demonstrate how ignoring sex differences in normalization introduces artificial bias in autosomal CpGs.
• To present a novel strategy for accurate normalization of both autosomal and sex chromosome data.

Main Methods:

• A novel two-step normalization strategy, interpolatedXY, is introduced.
• Autosomal CpGs are normalized independently using conventional methods (e.g., funnorm, dasen).
• Sex chromosome-linked CpGs are normalized by estimating corrected methylation values as a weighted average of their autosomal neighbors.

Main Results:

• The interpolatedXY strategy effectively reduces artificial sex bias introduced by conventional methods.
• The method is applicable to various quantile-based and non-quantile-based normalization techniques.
• A new metric, the sex explained fraction of variance, is proposed to quantify normalization effectiveness.

Conclusions:

• The proposed two-step normalization strategy provides an unbiased approach for analyzing sex chromosome methylation data.
• This method improves the accuracy of methylation analysis, particularly for autosomal CpGs affected by sex bias.
• The adjustedDasen and adjustedFunnorm functions are available in the wateRmelon package for practical implementation.