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Alternative empirical Bayes models for adjusting for batch effects in…
Alternative empirical Bayes models for adjusting for batch effects in genomics studies
Abstract
Contributing multiple methods for improved combination and analysis of data
Background
Methods
Combat batch adjustment
Moment-based diagnostics for batch effects
Sample-level moments
Gene-level moments
Robust F-test
Mean-only adjustment for batch effects
Reference batch adjustments
Software implementation
Dataset simulation
Pathway simulation
Bladder cancer
Nitric oxide
Oncogenic signature
Lung cancer
Results
Moments-based tests of significance for batch effects
Mean-only batch adjustments
Selecting the appropriate ComBat version for each dataset
Higher order moment-based batch adjustments
Batch adjustments based on a reference batch
Simulation study
EGFR signature and drug prediction
Discussion
Conclusions
Proposed diagnostic tools
Significance test for batch differences
Improved models based on ComBat
For certain situations
Proposed approach
Mean only ComBat for less severe adjustment
Reference-batch can leave one unchanged - Useful for reproducibility, for instance in context of biomarkers
Outperforms ComBat in particular cases