data.frame with the significance of P-values in the analyzed regions, dividing them into bins.
tabulate.pvals(input.regions = "all chrs", adjust.method = "BY", bins = c(0.001, 0.005, 0.01, 0.025, 0.05, 0.075, 0.1, 0.2, 1), significance.idx = 8, order.by, decreasing = TRUE, method = c("full", "smooth", "window", "overlap"), run.name = "analysis_results")vector indicating the dependent regions to be analyzed. Can be defined in four ways:
1) predefined input region: insert a predefined input region, choices are:
“all chrs”,
“all chrs auto”,
“all arms”,
“all arms auto”
In the predefined regions “all arms” and “all arms auto” the arms 13p, 14p, 15p, 21p and 22p
are left out, because in most studies there are no or few probes in these regions.
To include them, just make your own vector of arms.
2) whole chromosome(s): insert a single chromosome or a list of chromosomes as a
vector:
c(1, 2, 3).
3) chromosome arms: insert a single chromosome arm or a list of chromosome arms like
c("1q", "2p", "2q").
4) subregions of a chromosome: insert a chromosome number followed by the start and end position like
"chr1:1-1000000"
These regions can also be combined, e.g. c("chr1:1-1000000","2q", 3).
See integrated.analysis for more information.vector of significance thresholds. This function will calculate
the number of features having a P-value lower than the bin.integrated.analysis.integrated.analysisdata.frame. Each row corresponds to a chromosome and has
as many entries as entries in bins, plus 1. Each entry contains the
number of P-values that is smaller or equal to the corresponding entry
in bins.The last entry holds the percentage of P-values that is smaller than or
equal to the bin identified by significance.idx.
#first run example(assemble.data)
#and example(integrated.analysis)
tabulate.pvals(input.regions="8q",
adjust.method="BY",
bins=c(0.001,0.005,0.01,0.025,0.05,0.075,0.10,0.20,1.0),
run.name="chr8q")
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