fabatchaddon: Addon batch effect adjustment using FAbatch

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

Addon batch effect adjustment using FAbatch

Description

Performs addon batch effect adjustment using FAbatch. Takes the output of performing fabatch on a training data set and new batch data and correspondingly adjusts the test data to better match the adjusted training data according to the FAbatch model.

Usage

fabatchaddon(params, x, batch)

Arguments

params	object of class fabatch. Contains parameters necessary for addon batch effect adjustment.
x	matrix. The covariate matrix of the new data. Observations in rows, variables in columns.
batch	factor. Batch variable of the new data. Currently has to have levels: '1', '2', '3' and so on.

Value

The adjusted covariate matrix of the test data.

Note

It is not recommended to perform FAbatch in cross-study prediction settings, because it has been observed to (strongly) impair prediction performance. Given a not too small test set, the following methods are recommended (Hornung et al., 2016b): combatba, meancenter, ratioa, ratiog.

Author(s)

Roman Hornung

References

Hornung, R., Boulesteix, A.-L., Causeur, D. (2016). Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics 17:27, <doi: 10.1186/s12859-015-0870-z>.

Hornung, R., Causeur, D., Bernau, C., Boulesteix, A.-L. (2017). Improving cross-study prediction through addon batch effect adjustment and addon normalization. Bioinformatics 33(3):397–404, <doi: 10.1093/bioinformatics/btw650>.

Examples

data(autism)

# Random subset of 150 variables:
set.seed(1234)
Xsub <- X[,sample(1:ncol(X), size=150)]

# In cases of batches with more than 20 observations
# select 20 observations at random:
subinds <- unlist(sapply(1:length(levels(batch)), function(x) {
  indbatch <- which(batch==x)
  if(length(indbatch) > 20)
    indbatch <- sort(sample(indbatch, size=20))
  indbatch
}))
Xsub <- Xsub[subinds,]
batchsub <- batch[subinds]
ysub <- y[subinds]



trainind <- which(batchsub %in% c(1,2))

Xsubtrain <- Xsub[trainind,]
ysubtrain <- ysub[trainind]
batchsubtrain <- factor(as.numeric(batchsub[trainind]), levels=c(1,2))


testind <- which(batchsub %in% c(3,4))

Xsubtest <- Xsub[testind,]
ysubtest <- ysub[testind]

batchsubtest <- as.numeric(batchsub[testind])
batchsubtest[batchsubtest==3] <- 1
batchsubtest[batchsubtest==4] <- 2
batchsubtest <- factor(batchsubtest, levels=c(1,2))



params <- fabatch(x=Xsubtrain, y=ysubtrain, batch=batchsubtrain)

Xsubtestaddon <- fabatchaddon(params, x=Xsubtest, 
  batch=batchsubtest)

bapred documentation built on June 22, 2022, 9:08 a.m.