Batch Effect Removal (in Phenotype Prediction using Gene Data)
Description
Various tools dealing with batch effects, in particular enabling the
removal of discrepancies between training and test sets in prediction scenarios.
The following batch effect removal methods are implemented: FAbatch, ComBat, (f)SVA,
mean-centering, standardization, Ratio-A and Ratio-G. For each of these we provide
an additional function which enables a posteriori ('addon') batch effect removal
in independent batches ('test data'). Here, the (already batch effect adjusted)
training data is not altered. For evaluating the success of batch effect adjustment
several metrics are provided. Moreover, the package implements a plot for the
visualization of batch effects using principal component analysis. The main functions
of the package are ba() and baaddon() which enable batch effect removal and addon
batch effect removal, respectively, with one of the seven methods mentioned above.
Another important function is bametric() which is a wrapper function for all implemented
methods for evaluating the success of batch effect removal.