Peer Review Report For: methyvim: Targeted, robust, and model-free differential methylation analysis in R [version 1; peer review: 1 approved with reservations, 2 not approved]
Year: 2019
Type: peer-review
Abstract: We present methyvim , an R package implementing an algorithm for the nonparametric estimation of the effects of exposures on DNA methylation at CpG sites throughout the genome, complete with straightforward statistical inference for such estimates. The approach leverages variable importance measures derived from statistical parameters arising in causal inference, defined in such a manner that they may be used to obtain targeted estimates of the relative importance of individual CpG sites with respect to a binary treatment assigned at the phenotype level, thereby providing a new approach to identifying differentially methylated positions. The procedure implemented is computationally efficient, incorporating a preliminary screening step to isolate a subset of sites for which there is cursory evidence of differential methylation as well as a unique multiple testing correction to control the False Discovery Rate with the same rigor as would be available if all sites were subjected to testing. This novel technique for analysis of differentially methylated positions provides an avenue for incorporating flexible state-of-the-art data-adaptive regression procedures (i.e., machine learning) into the estimation of differential methylation effects without the loss of interpretable statistical inference for the estimated quantity.
Institution: University of California, Berkeley
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W4416619745
Cites:
Cited by:
Related to: