<p>See dataset <code><a rd-options="" href="/link/autism?package=bapred&version=1.1" data-mini-rdoc="bapred::autism">autism</a></code></p>

Various tools dealing with batch effects, in particular enabling the
removal of discrepancies between training and test sets in prediction scenarios.
Moreover, addon quantile normalization and addon RMA normalization (Kostka & Spang,
2008) is implemented to enable integrating the quantile normalization step into
prediction rules. 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 for batch effect adjustment
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 here is bametric() which is a wrapper function for all implemented
methods for evaluating the success of batch effect removal. For (addon) quantile
normalization and (addon) RMA normalization the functions qunormtrain(),
qunormaddon(), rmatrain() and rmaaddon() can be used.

Roman Hornung

bapred

Batch Effect Removal and Addon Normalization (in Phenotype
Prediction using Gene Data)

X function

<p>See dataset <code><a rd-options='' href='autism'>autism</a></code></p>

X: Covariate matrix of dataset `autism`

Description

Arguments