Dataset Information

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 (? 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 performances on the popular ?- and M-value representations of methylation levels. Overall, ?-values enable better imputation performances than M-values. Imputation accuracy is lower for mid-range ?-values, while it is generally more accurate for values at the extremes of the ?-value range. The MAR values distribution is on the average more dense in the mid-range in comparison to the expected ?-value distribution. As a consequence, MAR values are on average harder to impute.

Conclusions

The results of the analysis provide guidelines for the most suitable imputation approaches for DNA methylation data under different representations of DNA methylation levels and different missing data mechanisms.

SUBMITTER: Lena PD

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

REPOSITORIES: biostudies-literature

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Publications

Methylation data imputation performances under different representations and missingness patterns.

Lena Pietro Di PD Sala Claudia C Prodi Andrea A Nardini Christine C

BMC bioinformatics 20200629 1

<h4>Background</h4>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 D ...[more]

PMID: 32600298

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Dataset Information

Methylation data imputation performances under different representations and missingness patterns.

Background

Results

Conclusions

Publications

Methylation data imputation performances under different representations and missingness patterns.

Similar Datasets

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