TCGAbiolinks (version 1.2.5)

calculate.pvalues: Calculate pvalues

Description

Calculate pvalues using wilcoxon test

Usage

calculate.pvalues(data, groupCol = NULL, group1 = NULL, group2 = NULL, paired = FALSE, method = "BH", exact = TRUE, cores = 1)

Arguments

data

SummarizedExperiment obtained from the TCGAPrepare

groupCol

Columns with the groups inside the SummarizedExperiment object. (This will be obtained by the function colData(data))

group1

In case our object has more than 2 groups, you should set the groups

group2

In case our object has more than 2 groups, you should set the groups

paired

Do a paired wilcoxon test? Default: True

method

P-value adjustment method. Default:"BH" Benjamini-Hochberg

exact

Do a exact wilcoxon test? Default: True

cores

Number of cores to be used

Value

Data frame with cols p values/p values adjustedData frame with two cols p-values/p-values adjusted

Details

Verify if the data is significant between two groups. For the methylation we search for probes that have a difference in the mean methylation and also a significant value. Input: A SummarizedExperiment object that will be used to compared two groups with wilcoxon test, a boolean value to do a paired or non-paired test Output: p-values (non-adj/adj) histograms, p-values (non-adj/adj)

Examples

## Not run: 
# nrows <- 200; ncols <- 20
# counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
# rowRanges <- GenomicRanges::GRanges(rep(c("chr1", "chr2"), c(50, 150)),
#                    IRanges::IRanges(floor(runif(200, 1e5, 1e6)), width=100),
#                     strand=sample(c("+", "-"), 200, TRUE),
#                     feature_id=sprintf("ID%03d", 1:200))
# colData <- S4Vectors::DataFrame(Treatment=rep(c("ChIP", "Input"), 10),
#                     row.names=LETTERS[1:20],
#                     group=rep(c("group1","group2"),c(10,10)))
# data <- SummarizedExperiment::SummarizedExperiment(
#          assays=S4Vectors::SimpleList(counts=counts),
#          rowRanges=rowRanges,
#          colData=colData)
# data <- calculate.pvalues(data,"group")
# ## End(Not run)