Epigenetic ageing clocks: statistical methods and emerging computational challenges.

Area of Science:

• Biotechnology
• Computational Biology
• Gerontology

Background:

• Epigenetic clocks are machine learning tools estimating chronological and biological age.
• They are used to assess anti-aging, cellular rejuvenation, and disease prevention interventions.
• Current limitations exist in understanding, interpreting, and applying these clocks due to computational and statistical challenges.

Purpose of the Study:

• To review computational challenges in epigenetic clock development and application.
• To focus on interpretation, cell-type heterogeneity, and single-cell methodologies.
• To provide guidelines for constructing interpretable epigenetic clocks at cellular and single-cell resolutions.

Main Methods:

• Literature review of computational and statistical challenges in epigenetic clocks.
• Analysis of interpretation issues in epigenetic clock models.
• Examination of cell-type heterogeneity and its impact on clock accuracy.
• Review of emerging single-cell epigenetic clock methodologies.

Main Results:

• Identified significant computational and statistical hurdles hindering epigenetic clock applications.
• Highlighted challenges in interpreting epigenetic clock outputs.
• Discussed the impact of cell-type heterogeneity on clock accuracy.
• Reviewed advancements in single-cell epigenetic clock methods.

Conclusions:

• Addressing computational challenges is crucial for advancing epigenetic clock technology.
• Guidelines are proposed for developing more interpretable epigenetic clocks.
• Future directions include refining clocks for cell-type and single-cell resolutions.