Biology subjects

Barth, J. P.

Publications and source records attributed to Barth, J. P..

2 recordsLinked to original sources

bioRxiv2024

EpipwR: Efficient Power Analysis for EWAS withContinuous Outcomes

MotivationEpigenome-wide association studies (EWAS) have emerged as a popular way to investigate the pathophysiology of complex diseases and to assist in bridging the gap between genotypes and phenotypes. Despite the increasing popularity of EWAS, very few tools exist to aid researchers in power estimation and those are limited to case-control studies. The existence of user-friendly tools, expanding power calculation functionality to additional study designs would be a significant aid to researchers planning EWAS. ResultsWe introduce EpipwR, an open-source R package that can efficiently estimate power for EWAS with continuous outcomes. EpipwR uses a quasi-simulated approach, meaning that data is generated only for CpG sites with methylation associated with the outcome, while p-values are generated directly for those with no association (when necessary). Like existing EWAS power calculators, reference datasets of empirical EWAS are used to guide the data generation process. Two simulation studies show the effect of the selected empirical dataset on the generated correlations and the relative speed of EpipwR compared to similar approaches. Availability and ImplementationThe EpipwR R-package is currently available for download at github.com/jbarth216/EpipwR.

genomics↗

bioRxiv2023

MetaNorm: Incorporating Meta-analytic Priors into Normalization of NanoString nCounter Data

Non-informative or diffuse prior distributions are widely employed in Bayesian data analysis to maintain objectivity. However, when meaningful prior information exists and can be identified, using an informative prior distribution to accurately reflect current knowledge may lead to superior outcomes and great efficiency. We propose MetaNorm, a Bayesian algorithm for normalizing NanoString nCounter gene expression data. MetaNorm is based on RCRnorm, a powerful method designed under an integrated series of hierarchical models that allow various sources of error to be explained by different types of probes in the nCounter system. However, a lack of accurate prior information, weak computational efficiency, and instability of estimates that sometimes occur weakens the approach despite its impressive performance. MetaNorm employs priors carefully constructed from a rigorous meta-analysis to leverage information from large public data. Combined with additional algorithmic enhancements, MetaNorm improves RCRnorm by yielding more stable estimation of normalized values, better convergence diagnostics and superior computational efficiency. R Code for replicating the meta-analysis and the normalization function can be found at github.com/jbarth216/MetaNorm.

bioinformatics↗