Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses.
Removal of, or adjustment for, batch effects or center differences is generally required when such effects are present in data. In particular, when preparing microarray gene expression data from multiple cohorts, array platforms, or batches for later analyses, batch effects can have confounding effects, inducing spurious differences between study groups. Many methods and tools exist for removing batch effects from data. However, when study groups are not evenly distributed across batches, actual group differences may induce apparent batch differences, in which case batch adjustments may bias, usually deflate, group differences. Some tools therefore have the option of preserving the difference between study groups, e.g. using a two-way ANOVA model to simultaneously estimate both group and batch effects. Unfortunately, this approach may systematically induce incorrect group differences in downstream analyses when groups are distributed between the batches in an unbalanced manner. The scientific community seems to be largely unaware of how this approach may lead to false discoveries.
Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital HF - Radiumhospitalet, Montebello, 0310 Oslo, Norway.
Vegard
V
Nygaard
Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital HF - Radiumhospitalet, Montebello, 0310 Oslo, Norway.
Einar Andreas
EA
Rødland
Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital HF - Radiumhospitalet, Montebello, 0310 Oslo, Norway; Institute of Cancer Genetics and Informatics, Oslo University Hospital HF - Radiumhospitalet, Montebello, 0310 Oslo, Norway and Department of Informatics, University of Oslo, 0316 Oslo, Norway ehovig@ifi.uio.no.
Eivind
E
Hovig
D003627:Data Interpretation, Statistical; D006801:Humans; D046228:Microarray Analysis / Q000379:methods / Q000592:standards*; D015203:Reproducibility of Results
Batch effects; Data normalization; Microarrays; Reproducible research