SEMbap: Bow-free covariance search and data de-correlation.

ABSTRACT: Large-scale studies of gene expression are commonly influenced by biological and technical sources of expression variation, including batch effects, sample characteristics, and environmental impacts. Learning the causal relationships between observable variables may be challenging in the presence of unobserved confounders. Furthermore, many high-dimensional regression techniques may perform worse. In fact, controlling for unobserved confounding variables is essential, and many deconfounding methods have been suggested for application in a variety of situations. The main contribution of this article is the development of a two-stage deconfounding procedure based on Bow-free Acyclic Paths (BAP) search developed into the framework of Structural Equation Models (SEM), called SEMbap(). In the f

SUBMITTER: Grassi M 

PROVIDER: S-EPMC11419354 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature