Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE).

ABSTRACT: Batch-effects present challenges in the analysis of high-throughput molecular data and are particularly problematic in longitudinal studies when interest lies in identifying genes/features whose expression changes over time, but time is confounded with batch. While many methods to correct for batch-effects exist, most assume independence across samples; an assumption that is unlikely to hold in longitudinal microarray studies. We propose Batch effect Reduction of mIcroarray data with Dependent samples usinGEmpirical Bayes (BRIDGE), a three-step parametric empirical Bayes approach that leverages technical replicate samples profiled at multiple timepoints/batches, so-called "bridge samples", to inform batch-effect reduction/attenuation in long

SUBMITTER: Xia Q 

PROVIDER: S-EPMC9617207 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature