Normalization / Batch effects (sva)


Removes batch effect from the data using Combat algorithm.



This tool removes random (batch) effects, e.g. where samples cluster according to preparation day/laboratory instead of the biological group under study, using methodology described in Johnson et al. 2007. The method, called ComBat, uses either parametric or non-parametric empirical Bayes frameworks for adjusting data for batch effects. In order to use this tool, you need to add new phenodata column(s) where you indicate the random effect with numbers (e.g. 1 = laboratory one, 2 = laboratory two, 3 = laboratory three, etc). Please note that this kind of random effects modelling is recommended only when each random effect groups contains multiple arrays.


A tab-delimited text file containing gene names, expression estimates that has been corrected for batch effects and call values ("flags"). This file is suitable for all further analyses.


This tool uses Bioconductor package sva. Please cite the following articles:

Johnson WE, Li C and Rabinovic A (2007)Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8:118-27.