dmc.non.parametric.se {TCGAbiolinks} | R Documentation |
Calculate pvalues using wilcoxon test
dmc.non.parametric.se( data, groupCol = NULL, group1 = NULL, group2 = NULL, paired = FALSE, adj.method = "BH", alternative = "two.sided", cores = 1 )
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 |
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)
Data frame with cols p values/p values adjusted
Data frame with two cols p-values/p-values adjusted
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")