Methylation data imputation performances under different representations and missingness patterns.

ABSTRACT:

Background

High-throughput technologies enable the cost-effective collection and analysis of DNA methylation data throughout the human genome. This naturally entails missing values management that can complicate the analysis of the data. Several general and specific imputation methods are suitable for DNA methylation data. However, there are no detailed studies of their performances under different missing data mechanisms -(completely) at random or not- and different representations of DNA methylation levels (β and M-value).

Results

We make an extensive analysis of the imputation performances of seven imputation methods on simulated missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR) methylation data. We further consider imputation p

SUBMITTER: Lena PD 

PROVIDER: S-EPMC7325236 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature