bapred (version 1.1)

ba: Batch effect adjustment using a method of choice

Description

Performs batch effect adjustment using one of the following methods: FAbatch, ComBat, SVA, mean-centering, standardization, Ratio-A, Ratio-G or "no batch effect adjustment". Additionally returns information necessary for addon batch effect adjustment with the respective method. The latter can be done using baaddon.

Usage

ba(x, y, batch, method = c("fabatch", "combat", "sva", "meancenter", 
  "standardize", "ratioa", "ratiog", "none"), ...)

Arguments

x

matrix. The covariate matrix. observations in rows, variables in columns.

y

factor. Binary target variable. Has to have two factor levels, where each of them correponds to one of the two classes of the target variable. Only used for method = "fabatch" and method = "sva".

batch

factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch.

method

character. Batch effect adjustment method.

…

additional arguments to be passed to fabatch or svaba.

Value

The output of fabatch, combatba, svaba, meancenter, standardize, ratioa, ratiog or noba respectively.

Details

This function is merely for convenience - a wrapper function for fabatch, combatba, svaba, meancenter, standardize, ratioa, ratiog and noba.

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, <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<U+2013>404, <10.1093/bioinformatics/btw650>.

Johnson, W. E., Rabinovic, A., Li, C. (2007). Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics 8:118-127, <10.1093/biostatistics/kxj037>.

Leek, J. T., Storey, J. D. (2007). Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis. PLoS Genetics 3:1724-1735, <10.1371/journal.pgen.0030161>.

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, <10.1038/tpj.2010.57>.

Parker, H. S., Bravo, H. C., Leek, J. T. (2014). Removing batch effects for prediction problems with frozen surrogate variable analysis. PeerJ 2:e561, <10.7717/peerj.561>.

Examples

# NOT RUN {
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]


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

adjusteddata <- list()

for(i in seq(along=somemethods)) {
  cat(paste("Adjusting using method = \"", somemethods[i], "\"", 
    sep=""), "\n")
  adjusteddata[[i]] <- ba(x=Xsub, y=ysub, batch=batchsub, 
    method = somemethods[i])$xadj
}
# }