baaddon {bapred}R Documentation

Addon batch effect adjustment

Description

Performs addon batch effect adjustment for a method of choice: takes the output of ba or that of one of the functions performing a specific batch effect adjustment method (e.g. fabatch or svaba) and new batch data. Then performs the respective batch effect adjustment method on the new batch data.

Usage

baaddon(params, x, batch)

Arguments

params

object of class fabatch, combat, svatrain, meancenter, standardize, ratioa, ratiog or noba. Contains parameters necessary for addon batch effect adjustment according to the respective method.

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

The following methods are NOT recommended in cross-study prediction settings: FAbatch (fabatch), frozen SVA (svaba), standardization (standardize) as well as no addon batch effect adjustment (noba).

Given a not too small test set, the following methods are recommended (Hornung et al., 2016b): ComBat (combatba), mean-centering (meancenter), Ratio-A (ratioa), Ratio-G (ratiog).

Author(s)

Roman Hornung

References

Hornung, R., Boulesteix, A.-L., Causeur, D. (2016a) Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics 17:27.

Hornung, R., Causeur, D., Bernau, C., Boulesteix, A.-L. (2016b). Improving cross-study prediction through addon batch effect adjustment and addon normalization. Technical Report, Department of Statistics, LMU.

Johnson, W. E., Rabinovic, A., Li, C. (2007) Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics, 8, 118-127.

Leek, J. T., Storey, J. D. (2007) Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis. PLoS Genetics, 3, 1724–1735.

Luo, J., Schumacher, M., Scherer, A., Sanoudou, D., Megherbi, D., Davison, T., Shi, T., Tong, W., Shi, L., Hong, H., Zhao, C., Elloumi, F., Shi, W., Thomas, R., Lin, S., Tillinghast, G., Liu, G., Zhou, Y., Herman, D., Li, Y., Deng, Y., Fang, H., Bushel, P., Woods, M., Zhang, J. (2010) A comparison of batch effect removal methods for enhancement of prediction performance using maqc-ii microarray gene expression data. The Pharmacogenomics Journal, 10, 278-291.

Parker, H. S., Bravo, H. C., Leek, J. T. (2014) Removing batch effects for prediction problems with frozen surrogate variable analysis. PeerJ, 2, e561.

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))



somemethods <- c("fabatch", "combat", "meancenter", "none")

adjustedtestdata <- list()

for(i in seq(along=somemethods)) {
  cat(paste("Adjusting training data using method = \"", somemethods[i], 
    "\"", sep=""), "\n")
  paramstemp <- ba(x=Xsubtrain, y=ysubtrain, batch=batchsubtrain, 
    method = somemethods[i])
  cat(paste("Addon adjusting test data using method = \"", 
    somemethods[i], "\"", sep=""), "\n")
  adjustedtestdata[[i]] <- baaddon(params=paramstemp, x=Xsubtest, 
    batch=batchsubtest)
}

[Package bapred version 1.0 Index]