Package: bapred 1.1

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.

Authors:Roman Hornung, David Causeur

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bapred.pdf |bapred.html
bapred/json (API)

# Install 'bapred' in R:
install.packages('bapred', repos = c('https://romanhornung.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • X - Covariate matrix of dataset 'autism'
  • batch - Batch variable of dataset 'autism'
  • y - Target variable of dataset 'autism'

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.78 score 20 scripts 271 downloads 3 mentions 59 exports 83 dependencies

Last updated 2 years agofrom:e24720be3c. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-winOKNov 20 2024
R-4.5-linuxOKNov 20 2024
R-4.4-winOKNov 20 2024
R-4.4-macOKNov 20 2024
R-4.3-winOKNov 20 2024
R-4.3-macOKNov 20 2024

Exports:aprioravedistavedistTwobabaaddonbametricbivprobbpriorbuild.designcombatbacombatbaaddoncorbadesign.matdiffexprmdiffexprmAfterBRDtemfahighdimextractAffybatchfabatchfabatchaddonfuzzywilcoxit.solkldistkldistTwolist.batchmeancentermeancenteraddonmypvcaBatchAssessnbfactorsnobanobaaddonnormalizeAffyBatchqntvalnormalizeqntaddnormalizeqntadd2normalizeqntvalpcplotplotcomppostmeanpostvarpvcamqunormaddonqunormtrainratioaratioaaddonratiogratiogaddonrmaaddonrmatrainsepscoresepscoreTwoskewdivskewdivTwostandardizestandardizeaddonsummarizeadd2summarizeval2svabasvabaaddonVarInflation

Dependencies:affyaffyioaffyPLMannotateAnnotationDbiaskpassBHBiobaseBiocGenericsBiocManagerBiocParallelBiostringsbitbit64blobbootcachemclicodetoolscpp11crayoncurlDBIedgeRfastmapFNNforeachformatRfutile.loggerfutile.optionsfuzzyRankTestsgcrmagenefiltergenericsGenomeInfoDbGenomeInfoDbDataglmnetgluehttrIRangesiteratorsjsonliteKEGGRESTlambda.rlatticelifecyclelimmalme4locfitMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimeminqamnormtnlmenloptropensslpkgconfigplogrpngpreprocessCoreR6RcppRcppEigenrlangRSQLiteS4VectorsshapesnowstatmodsurvivalsvasysUCSC.utilsvctrsXMLxtableXVectorzlibbioc

Readme and manuals

Help Manual

Help pageTopics
The bapred packagebapred-package bapred
Autism datasetautism
Average minimal distance between batchesavedist
Batch effect adjustment using a method of choiceba
Addon batch effect adjustmentbaaddon
Diverse metrics for quality of (adjusted) batch databametric
batch variable of dataset 'autism'batch
Batch effect adjustment using ComBatcombatba
Addon batch effect adjustment using ComBatcombatbaaddon
Mean correlation before and after batch effect adjustmentcorba
Measure for performance of differential expression analysis (after batch effect adjustment)diffexprm
Batch effect adjustment using FAbatchfabatch
Addon batch effect adjustment using FAbatchfabatchaddon
Kullback-Leibler divergence between density of within and between batch pairwise distanceskldist
Batch effect adjustment by mean-centeringmeancenter
Addon batch effect adjustment for mean-centeringmeancenteraddon
No batch effect adjustmentnoba
No addon batch effect adjustmentnobaaddon
Visualization of batch effects using Principal Component Analysispcplot
Proportion of variation induced by class signal estimated by Principal Variance Component Analysispvcam
Addon quantile normalization using ``documentation by value'' (Kostka & Spang, 2008)qunormaddon
Quantile normalization with ``documentation by value'' (Kostka & Spang, 2008)qunormtrain
Batch effect adjustment using Ratio-Aratioa
Addon batch effect adjustment for Ratio-Aratioaaddon
Batch effect adjustment using Ratio-Gratiog
Addon batch effect adjustment for Ratio-Gratiogaddon
Addon RMA normalization using ``documentation by value'' (Kostka & Spang, 2008)rmaaddon
RMA normalization with ``documentation by value'' (Kostka & Spang, 2008)rmatrain
Separation score as described in Hornung et al. (2016)sepscore
Skewness divergence scoreskewdiv
Batch effect adjustment by standardizationstandardize
Addon batch effect adjustment for standardizationstandardizeaddon
Batch effect adjustment using SVAsvaba
Addon batch effect adjustment using frozen SVAsvabaaddon
Covariate matrix of dataset 'autism'X
Target variable of dataset 'autism'y