Compares the distribution of all beta values corresponding to one batch with the distribution of all beta values corresponding to all other batches and retuns a pvalue which defines if the distributions are the same or not. Standard two sided KolmogorovSmirnov test is used to calculate the (adjusted) pvalues.
1  calcPvalues(data, samples, parallel=TRUE, cores=4, adjusted=TRUE, method="fdr")

data 
any matrix filled with beta values, column names have to be sample_ids corresponding to the ids listed in "samples", row names have to be gene names. 
samples 
data frame with two columns, the first column has to contain the sample numbers, the second column has to contain the corresponding batch number. Colnames have to be named as "sample_id" and "batch_id". 
parallel 
should the calculation be done in parallel mode?

cores 
if running in parallel mode, define the number of cores used
for the calculation. 
adjusted 
should the pvalues be adjusted or not, see "method" for available adjustment methods. 
method 
adjustment method for pvalue adjustment (if TRUE), default
method is "false discovery rate adjustment", for other available methods
see the description of the used standard R package 
a matrix containing pvalues for all genes in all batches, the column names define the batch numbers, row names are the same gene names as contained in the input matrix.
snowfall
ks.test
p.adjust
correctBatchEffect
1 2 3 4 5 6 7 8 9 10  ## Shortly running example. For a more realistic example that takes
## some more time, run the same procedure with the full BEclearData
## dataset.
## Calculate fdradjusted pvalues in nonparallel mode
data(BEclearData)
ex.data < ex.data[31:90,7:26]
ex.samples < ex.samples[7:26,]
pvals < calcPvalues(data=ex.data, samples=ex.samples, parallel=FALSE,
adjusted=TRUE, method="fdr")

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