Create an evidence plot for a module
evidencePlot(
l,
m,
mset = "all",
rug = TRUE,
roc = TRUE,
filter = FALSE,
unique = TRUE,
add = FALSE,
col = "black",
col.rug = "#eeeeee",
gene.labels = NULL,
gene.colors = NULL,
gene.lines = 1,
gl.cex = 1,
style = "roc",
lwd = 1,
lty = 1,
rug.size = 0.2,
legend = NULL,
...
)sorted list of HGNC gene identifiers
character vector of modules for which the plot should be created
Which module set to use (see tmodUtest for details)
if TRUE, draw a rug-plot beneath the ROC curve
if TRUE, draw a ROC curve above the rug-plot
if TRUE, genes not defined in the module set will be removed
if TRUE, duplicates will be removed
if TRUE, the plot will be added to the existing plot
a character vector color to be used
a character value specifying the color of the rug
if TRUE, gene names are shown; alternatively, a named character vector with gene labels to be shown, or NULL (default) for no labels (option evaluated only if rug is plotted)
NULL (default) or a character vectors indicating the color for each gene. Either a named vector or a vector with the same order of genes as `l`.
a number or a vector of numbers; line width for marking the genes on the rug (default=1). If the vector is named, the names should be gene ids.
Text cex (magnification) for gene labels
"roc" for receiver-operator characteristic curve (default), and "gsea" for GSEA-style (Kaplan-Meier like plot)
line width (see par())
line type (see par())
fraction of the plot that should show the rug. If rug.size is 0, rug is not drawn. If rug.size is 1, ROC curve is not drawn.
position of the legend. If NULL, no legend will be drawn
Further parameters passed to the plotting function
This function creates an evidence plot for a module, based on an ordered list of genes. By default, the plot shows the receiving operator characteristic (ROC) curve and a rug below, which indicates the distribution of the module genes in the sorted list.
Several styles of the evidence plot are possible: * roc (default): a receiver-operator characteristic like curve; the area under the curve corresponds to the effect size (AUC) * roc_absolute: same as above, but the values are not scaled by the total number of genes in a module * gsea * enrichment: the curve shows relative enrichment at the given position
[tmod-package()], [hgEnrichmentPlot()]
# artificially enriched list of genes
set.seed(123)
data(tmod)
bg <- sample(tmod$gv)
fg <- getGenes("LI.M127", as.list=TRUE)[[1]]
fg <- sample(c(fg, bg[1:1000]))
l <- unique(c(fg, bg))
evidencePlot(l, "LI.M127")
evidencePlot(l, filter=TRUE, "LI.M127")
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