calcPvalues {BEclear} | R Documentation |
Compares the distribution of all beta values corresponding to one batch with the distribution of all beta values corresponding to all other batches and returns a p-value which defines if the distributions are the same or not. Standard two sided Kolmogorov-Smirnov test is used to calculate the (adjusted) p-values.
calcPvalues(data, samples, adjusted=TRUE, method="fdr", BPPARAM=bpparam())
data |
a |
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". |
adjusted |
should the p-values be adjusted or not, see "method" for available adjustment methods. |
method |
adjustment method for p-value adjustment (if TRUE), default
method is "false discovery rate adjustment", for other available methods
see the description of the used standard R package |
BPPARAM |
An instance of the
|
calcPvalues
a matrix containing p-values 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. If there are only missing values present for a gene in a batch, a p-values of 0 is returned
## Shortly running example. For a more realistic example that takes ## some more time, run the same procedure with the full BEclearData ## dataset. ## Calculate fdr-adjusted p-values in non-parallel mode data(BEclearData) ex.data <- ex.data[31:90,7:26] ex.samples <- ex.samples[7:26,] library(data.table) samples <- data.table(ex.samples) data <- data.table(feature=rownames(ex.data), ex.data) data <- melt(data = data, id.vars = "feature", variable.name = "sample", value.name = "beta.value") setkey(data, "feature", "sample") pvals <- calcPvalues(data=data, samples=samples,method="fdr")