Alternative empirical Bayes models for adjusting for batch effects in genomic studies.

ABSTRACT:

Background

Combining genomic data sets from multiple studies is advantageous to increase statistical power in studies where logistical considerations restrict sample size or require the sequential generation of data. However, significant technical heterogeneity is commonly observed across multiple batches of data that are generated from different processing or reagent batches, experimenters, protocols, or profiling platforms. These so-called batch effects often confound true biological relationships in the data, reducing the power benefits of combining multiple batches, and may even lead to spurious results in some combined studies. Therefore there is significant need for effective methods and software tools that account for batch effects in high-throughput genomic studies.

Resu

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC6044013 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature