dmc.non.parametric.se: Calculate pvalues

dmc.non.parametric.seR 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")

BioinformaticsFMRP/TCGAbiolinks documentation built on April 12, 2024, 2:08 a.m.