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.
|Author||Roman Hornung, David Causeur|
|Date of publication||2016-06-03 19:12:25|
|Maintainer||Roman Hornung <email@example.com>|
autism: Autism dataset
avedist: Average minimal distance between batches
ba: Batch effect adjustment using a method of choice
baaddon: Addon batch effect adjustment
bametric: Diverse metrics for quality of (adjusted) batch data
bapred-internal: Internal bapred functions
bapred-package: The bapred package
batch: batch variable of dataset 'autism'
combatba: Batch effect adjustment using ComBat
combatbaaddon: Addon batch effect adjustment using ComBat
corba: Mean correlation before and after batch effect adjustment
diffexprm: Measure for performance of differential expression analysis...
fabatch: Batch effect adjustment using FAbatch
fabatchaddon: Addon batch effect adjustment using FAbatch
kldist: Kullback-Leibler divergence between density of within and...
meancenter: Batch effect adjustment by mean-centering
meancenteraddon: Addon batch effect adjustment for mean-centering
noba: No batch effect adjustment
nobaaddon: No addon batch effect adjustment
pcplot: Visualization of batch effects using Principal Component...
pvcam: Proportion of variation induced by class signal estimated by...
qunormaddon: Addon quantile normalization using "documentation by value"...
qunormtrain: Quantile normalization with "documentation by value" (Kostka...
ratioa: Batch effect adjustment using Ratio-A
ratioaaddon: Addon batch effect adjustment for Ratio-A
ratiog: Batch effect adjustment using Ratio-G
ratiogaddon: Addon batch effect adjustment for Ratio-G
rmaaddon: Addon RMA normalization using "documentation by value"...
rmatrain: RMA normalization with "documentation by value" (Kostka &...
sepscore: Separation score as described in Hornung et al. (2016)
skewdiv: Skewness divergence score
standardize: Batch effect adjustment by standardization
standardizeaddon: Addon batch effect adjustment for standardization
svaba: Batch effect adjustment using SVA
svabaaddon: Addon batch effect adjustment using frozen SVA
X: Covariate matrix of dataset 'autism'
y: Target variable of dataset 'autism'