dmc.non.parametric.se {TCGAbiolinks} | R Documentation

## Calculate pvalues

### Description

Calculate pvalues using wilcoxon test

### Usage

    dmc.non.parametric.se(
      data,
      groupCol = NULL,
      group1 = NULL,
      group2 = NULL,
      paired = FALSE,
      adj.method = "BH",
      alternative = "two.sided",
      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
`adj.method` | P-value adjustment method. Default:"BH" Benjamini-Hochberg
`alternative` | wilcoxon test alternative
`cores` | Number of cores to be used

### 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)

### Value

Data frame with cols p values/p values adjusted

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

### Examples

    nrows <- 200; ncols <- 20
     counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows,
               dimnames = list(paste0("cg",1:200),LETTERS[1:20]))
    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)
    results <- TCGAbiolinks:::dmc.non.parametric.se(data,"group")

[Package TCGAbiolinks version 2.20.1 Index]