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Differential methylation values in differential methylation analysis.

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This study bridges the gap between statistically valid M-values and biologically interpretable β-values for DNA methylation analysis. It proposes methods to calculate differential methylation β-values (Δβ) from M-value linear regression models, offering more accurate results than traditional β-value methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • DNA methylation analysis commonly uses β-values and M-values.
  • M-values offer statistical validity for differential analysis, while β-values provide biological interpretability.
  • Calculating differential methylation β-values (Δβ) from M-value models is challenging.

Purpose of the Study:

  • To develop and compare methods for calculating biologically interpretable Δβ from statistically sound M-value linear regression models.
  • To provide a bridge between M-value statistical rigor and β-value biological interpretability in differential methylation analysis.
  • To evaluate the accuracy of novel Δβ calculation methods against traditional β-value regression.

Main Methods:

  • Proposed three methods to calculate Δβ from M-value linear regression.
  • Compared these methods with Δβ derived directly from β-value linear regression.
  • Evaluated method performance under varying M-value and β-value ranges.

Main Results:

  • The M-model-coef method is superior when the M-value linear regression model is accurate.
  • The M-model-M-mean method is a robust alternative, especially when model coefficients are unavailable.
  • Directly calculating Δβ from β-value linear regression can yield biased results, particularly outside specific M-value and β-value ranges.

Conclusions:

  • The proposed methods effectively translate M-value regression results into interpretable Δβ values.
  • M-model-coef and M-model-M-mean offer reliable approaches for differential methylation analysis.
  • Avoid direct Δβ calculation from β-value regression for potentially biased outcomes.