dmc.non.parametric.se: Calculate pvalues

In TCGAbiolinks: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data

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")

TCGAbiolinks documentation built on Nov. 8, 2020, 5:37 p.m.