Comparison of statistical methods and the use of quality control samples for batch effect correction in human transcriptome data.

ABSTRACT: Batch effects are technical sources of variation introduced by the necessity of conducting gene expression analyses on different dates due to the large number of biological samples in population-based studies. The aim of this study is to evaluate the performances of linear mixed models (LMM) and Combat in batch effect removal. We also assessed the utility of adding quality control samples in the study design as technical replicates. In order to do so, we simulated gene expression data by adding "treatment" and batch effects to a real gene expression dataset. The performances of LMM and Combat, with and without quality control samples, are assessed in terms of sensitivity and specificity while correcting for the batch effect using a wide range of effect sizes, statistical noise, sample size

SUBMITTER: Espin-Perez A 

PROVIDER: S-EPMC6117018 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature