HighDimMixedModels.jl: Robust high-dimensional mixed-effects models across omics data.

ABSTRACT: High-dimensional mixed-effects models are an increasingly important form of regression in which the number of covariates rivals or exceeds the number of samples, which are collected in groups or clusters. The penalized likelihood approach to fitting these models relies on a coordinate descent algorithm that lacks guarantees of convergence to a global optimum. Here, we empirically study the behavior of this algorithm on simulated and real examples of three types of data that are common in modern biology: transcriptome, genome-wide association, and microbiome data. Our simulations provide new insights into the algorithm's behavior in these settings, and, comparing the performance of two popular penalties, we demonstrate that the smoothly clipped absolute deviation (SCAD) penalty consistently

SUBMITTER: Gorstein E 

PROVIDER: S-EPMC11761659 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature