meancenter

matrix. The covariate matrix of the new data. Observations in rows, variables in columns.

factor. Batch variable. Currently has to have levels: '1', '2', '3' and so on.

batch

Performs batch effect adjustment by centering the variables within batches to have zero mean.

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.

meancenter: Batch effect adjustment by mean-centering

Description

Usage

Arguments

Value

Examples