bapred (version 0.3)

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.

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. Currently has to have levels '1' and '2'. Only used for method = "fabatch" and method = "sva".

batch

factor. Batch variable. Currently has to have levels: '1', '2', '3' and so on.

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. 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]


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

adjusteddata <- list()

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