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

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.

AuthorRoman Hornung, David Causeur
Date of publication2016-06-03 19:12:25
MaintainerRoman Hornung <>

View on CRAN

Man pages

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'

Files in this package

bapred/R/postvar.R bapred/R/normalizeqntval.R bapred/R/normalizeAffyBatchqntval.R bapred/R/fabatchaddon.R bapred/R/meancenteraddon.R bapred/R/rmatrain.R bapred/R/it.sol.R bapred/R/mypvcaBatchAssess.R bapred/R/print.ratioa.R bapred/R/print.noba.R bapred/R/svaba.R bapred/R/combatba.R bapred/R/qunormaddon.R bapred/R/ratioaaddon.R bapred/R/skewdiv.R bapred/R/aprior.R bapred/R/VarInflation.R bapred/R/print.combat.R bapred/R/bametric.R bapred/R/plotcomp.R bapred/R/svabaaddon.R bapred/R/print.fabatch.R bapred/R/meancenter.R bapred/R/normalizeqntadd.R bapred/R/print.meancenter.R bapred/R/print.svatrain.R bapred/R/rmaaddon.R bapred/R/qunormtrain.R bapred/R/diffexprmAfterBR.R bapred/R/design.mat.R bapred/R/summarizeadd2.R bapred/R/noba.R bapred/R/baaddon.R bapred/R/bprior.R bapred/R/pcplot.R bapred/R/pvcam.R bapred/R/standardize.R bapred/R/Dt.R bapred/R/sepscoreTwo.R bapred/R/postmean.R bapred/R/ bapred/R/skewdivTwo.R bapred/R/diffexprm.R bapred/R/print.standardize.R bapred/R/bivprob.R bapred/R/emfahighdim.R bapred/R/kldistTwo.R bapred/R/ratiogaddon.R bapred/R/combatbaaddon.R bapred/R/corba.R bapred/R/ratiog.R bapred/R/nobaaddon.R bapred/R/ratioa.R bapred/R/kldist.R bapred/R/print.qunormtrain.R bapred/R/sepscore.R bapred/R/avedistTwo.R bapred/R/nbfactors.R bapred/R/standardizeaddon.R bapred/R/ba.R bapred/R/print.rmatrain.R bapred/R/extractAffybatch.R bapred/R/list.batch.R bapred/R/fuzzywilcox.R bapred/R/ba-internal.R bapred/R/avedist.R bapred/R/print.ratiog.R bapred/R/normalizeqntadd2.R bapred/R/summarizeval2.R bapred/R/fabatch.R
bapred/man/pcplot.Rd bapred/man/skewdiv.Rd bapred/man/bapred-package.Rd bapred/man/diffexprm.Rd bapred/man/pvcam.Rd bapred/man/corba.Rd bapred/man/svaba.Rd bapred/man/nobaaddon.Rd bapred/man/standardizeaddon.Rd bapred/man/y.Rd bapred/man/batch.Rd bapred/man/qunormaddon.Rd bapred/man/ratioa.Rd bapred/man/rmaaddon.Rd bapred/man/kldist.Rd bapred/man/sepscore.Rd bapred/man/baaddon.Rd bapred/man/avedist.Rd bapred/man/ba.Rd bapred/man/fabatch.Rd bapred/man/bametric.Rd bapred/man/combatba.Rd bapred/man/meancenter.Rd bapred/man/noba.Rd bapred/man/bapred-internal.Rd bapred/man/ratioaaddon.Rd bapred/man/ratiogaddon.Rd bapred/man/meancenteraddon.Rd bapred/man/autism.Rd bapred/man/combatbaaddon.Rd bapred/man/X.Rd bapred/man/rmatrain.Rd bapred/man/fabatchaddon.Rd bapred/man/ratiog.Rd bapred/man/svabaaddon.Rd bapred/man/qunormtrain.Rd bapred/man/standardize.Rd

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