preputils (version 1.0.3)

rmbat: batch effect removal by mean centering and shifting

Description

Remove known categorical batch effects from high dimensional data sets

Usage

rmbat(x,batches)

Arguments

x

name of object to be processed. This is a matrix in atribute major format (rows correspond to variables, columns to observations)

batches

Vector with batch identifiers for each of the columns in x

Value

matrix with same dimensions as x and batch effects removed

Details

For each variable the mean values of all batches are shifted to the grand mean of the total sample. On case of several independent bacth effects being present in th data set, thes can either be combined in one batch variable, or the batches can be removed one at a time by chaining the processing and caling the cuntiong with each of the batch variables in turn

Examples

# NOT RUN {
    # create data set
    n_obs = 8
    n_var = 10
    predictor <- rep(0:1,n_obs*0.5)
    pure_effect <- outer(rnorm(n_var),predictor)
    error <- matrix(rnorm(n_var*n_obs),n_var,n_obs)
    batch1 <- rep(1:2,each=n_obs*0.5)
    batch2 <- rep(c(1,2,1,2),each=n_obs*0.25)
    batch_effect1 <- outer(rnorm(n_var)*2,scale(batch1))[,,1]
    batch_effect2 <- outer(rnorm(n_var)*4,scale(batch2))[,,1]
    batch_effect <- batch_effect1 + batch_effect2
    data_measured <- pure_effect + batch_effect + error
    
    zero = outer(rep(0,n_var),rep(0,n_obs))
    b1 <- rmbat(batch_effect1,batch1)
    b2 <- rmbat(batch_effect2,batch2)
    b12a <- rmbat(batch_effect1,paste(batch1,batch2))
    b12b <- batch_effect 
# }
# NOT RUN {
<!-- %>% rmbat(batch1) %>% rmbat(batch2) -->
# }
# NOT RUN {
    all.equal(b1,zero)
    all.equal(b2,zero)
    all.equal(b12a,zero)
    all.equal(b12b,zero)
# }