bapred (version 0.2)

baaddon: 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 adjust

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.

References

Hornung, R., Boulesteix, A.-L., Causeur, D. (2015) Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. Tech. Rep. 184, Department of Statistics, University of Munich. 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=""), "")
  paramstemp <- ba(x=Xsubtrain, y=ysubtrain, batch=batchsubtrain, 
    method = somemethods[i])
  cat(paste("Addon adjusting test data using method = "", 
    somemethods[i], """, sep=""), "")
  adjustedtestdata[[i]] <- baaddon(params=paramstemp, x=Xsubtest, 
    batch=batchsubtest)
}