Function for generating a concordance at the top plot, which compares concordance of the p-values of differential expression --- typically from the normalized or raw data set data.test --- to the p-values of differential expression of a gold standard --- typically the data set data.benchmark.

fig.CAT(DEA, truth.DEA, title, maxrank = 100, subset = NULL)

Arguments

DEA

Results of a differential expression analysis using the included methods DE.voom or DE.edgeR. Must be a list including

id.list

List of differentially expressed markers

p.val

p-values for differential expression

truth.DEA

Gold standard (assumed truth) for differential expression. Must be in the same format as DEA.

title

optional Figure title

maxrank

optional specify the maximum size of top-ranked items that you want to plot.

subset

optional vector of a subset of genes/markers for this analysis

Value

figure of concordance for comparison

Examples

voom.benchmark <- DE.voom(data.benchmark, data.group)
test.norm <- pip.norm(raw=data.test, groups=data.group, norm.method = "all")
#> converting counts to integer mode
#> Loading required package: Biobase
#> Loading required package: BiocGenerics
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#>     lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
#>     pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
#>     tapply, union, unique, unsplit, which.max, which.min
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test.DE <- list(
TMM = DE.voom(RC=test.norm$TMM$dat.normed, groups = data.group),
TC = DE.voom(RC=test.norm$TC$dat.normed, groups = data.group),
UQ = DE.voom(RC=test.norm$UQ$dat.normed, groups = data.group),
med = DE.voom(RC=test.norm$med$dat.normed, groups = data.group),
DESeq = DE.voom(RC=test.norm$DESeq$dat.normed, groups = data.group),
PoissonSeq = DE.voom(RC=test.norm$PoissonSeq$dat.normed, groups = data.group),
QN = DE.voom(RC=test.norm$QN$dat.normed, groups = data.group),
RUVg = DE.voom(RC=data.test, groups = data.group, normalized=FALSE, adjust=test.norm$RUVg$adjust.factor),
RUVs = DE.voom(RC=data.test, groups = data.group, normalized=FALSE, adjust=test.norm$RUVs$adjust.factor),
RUVr = DE.voom(RC=data.test, groups = data.group, normalized=FALSE, adjust=test.norm$RUVr$adjust.factor),
SVA = DE.voom(RC=data.test, groups = data.group, normalized=FALSE, adjust=test.norm$SVA$adjust.factor),
noNorm = DE.voom(RC=data.test, groups = data.group))

fig.CAT(DEA = test.DE, truth.DEA = voom.benchmark, title = "Example of CAT plot")