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Pseudotime Analysis Reveals Exponential Trends in DNA Methylation Aging with Mortality Associated Timescales
by
Farrell, Colin
, Lapborisuth, Kalsuda
, Pellegrini, Matteo
in
Age
/ Aging
/ Aging - genetics
/ Brain
/ Cell cycle
/ Datasets
/ DNA methylation
/ DNA Methylation - genetics
/ Epigenesis, Genetic
/ epigenetic aging
/ Epigenetics
/ Epigenomics - methods
/ Gene expression
/ Humans
/ Mortality
/ Pacemakers
/ pseudotime analysis
/ trajectory inference
/ Trends
2022
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Pseudotime Analysis Reveals Exponential Trends in DNA Methylation Aging with Mortality Associated Timescales
by
Farrell, Colin
, Lapborisuth, Kalsuda
, Pellegrini, Matteo
in
Age
/ Aging
/ Aging - genetics
/ Brain
/ Cell cycle
/ Datasets
/ DNA methylation
/ DNA Methylation - genetics
/ Epigenesis, Genetic
/ epigenetic aging
/ Epigenetics
/ Epigenomics - methods
/ Gene expression
/ Humans
/ Mortality
/ Pacemakers
/ pseudotime analysis
/ trajectory inference
/ Trends
2022
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Pseudotime Analysis Reveals Exponential Trends in DNA Methylation Aging with Mortality Associated Timescales
by
Farrell, Colin
, Lapborisuth, Kalsuda
, Pellegrini, Matteo
in
Age
/ Aging
/ Aging - genetics
/ Brain
/ Cell cycle
/ Datasets
/ DNA methylation
/ DNA Methylation - genetics
/ Epigenesis, Genetic
/ epigenetic aging
/ Epigenetics
/ Epigenomics - methods
/ Gene expression
/ Humans
/ Mortality
/ Pacemakers
/ pseudotime analysis
/ trajectory inference
/ Trends
2022
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Pseudotime Analysis Reveals Exponential Trends in DNA Methylation Aging with Mortality Associated Timescales
Journal Article
Pseudotime Analysis Reveals Exponential Trends in DNA Methylation Aging with Mortality Associated Timescales
2022
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Overview
The epigenetic trajectory of DNA methylation profiles has a nonlinear relationship with time, reflecting rapid changes in DNA methylation early in life that progressively slow with age. In this study, we use pseudotime analysis to determine the functional form of these trajectories. Unlike epigenetic clocks that constrain the functional form of methylation changes with time, pseudotime analysis orders samples along a path, based on similarities in a latent dimension, to provide an unbiased trajectory. We show that pseudotime analysis can be applied to DNA methylation in human blood and brain tissue and find that it is highly correlated with the epigenetic states described by the Epigenetic Pacemaker. Moreover, we show that the pseudotime trajectory can be modeled with respect to time, using a sum of two exponentials, with coefficients that are close to the timescales of human age-associated mortality. Thus, for the first time, we can identify age-associated molecular changes that appear to track the exponential dynamics of mortality risk.
Publisher
MDPI AG,MDPI
Subject
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