Overcoming the impacts of two-step batch effect correction on gene expression estimation and inference.

ABSTRACT: Nonignorable technical variation is commonly observed across data from multiple experimental runs, platforms, or studies. These so-called batch effects can lead to difficulty in merging data from multiple sources, as they can severely bias the outcome of the analysis. Many groups have developed approaches for removing batch effects from data, usually by accommodating batch variables into the analysis (one-step correction) or by preprocessing the data prior to the formal or final analysis (two-step correction). One-step correction is often desirable due it its simplicity, but its flexibility is limited and it can be difficult to include batch variables uniformly when an analysis has multiple stages. Two-step correction allows for richer models of batch mean and variance. However, prior inve

SUBMITTER: Li T 

PROVIDER: S-EPMC10449015 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature