Description Usage Arguments Details References See Also Examples
Plot ROC curve(s) for benchmarked network(s)
1 2 3 |
tpvsfp |
a list contaning data for the ROC curve(s) from |
mode |
either ROC curve (roc) or precision recall curve (prc). default: roc |
tpr.stop |
upper limit for the Y-axis (true positives). default: 1 |
fpr.stop |
upper limit for the X-axis (false prositives), default: 1 |
coff.z |
the point where to stop the ROC curve (in order to disable, set coff.z to below the minimal possible level) |
coff.fdr |
the point where to put the circle ('cross') at the ROC curve |
cex.main |
font size for the main title |
cex.leg |
font size for the legend |
main |
title name for the plot, default: none |
use.lty |
if different line types should be used for the curves, useful when the number of curves is very big (>10). default:"FALSE" |
print.stats |
add N(edges) and N(vertices) to the legend lines; only works if non-empty $ne and $nv elements are submitted in tpvsfp. default:"TRUE" |
sort_by_letter_and_remove |
enable a specific order for the curves in the legend, set by prefix letters |
The function roc
can be called either from inside benchmark
, or separately. In the latter case, it should receive the first argument as an object of the same type as benchmark
can return. Generally, a ROC curve encompasses the whole interval from 0% to 100% at both axes. However in case of predictions made via network analysis, only the interval with (at least) formally significant scores is of interest (Merid et.al 2014).
Therefore the benchmark results are presented as ROC curves that end at minimal acceptable confidence, set via either a z-score cut-off. Additionally, an extra point is denoted that might correspond to cross.z
in roc
. The scale is given in no. of tested member genes rather then as percentage of the total no. of tests. The scale ranges xlim
and ylim
of the plot can be reduced by submitting parameters tpr.stop
and fpr.stop
with values <1.
http://www.biomedcentral.com/1471-2105/15/308
benchmark
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | # Benchmark and plot one networks on the whole set of test GSs, using no mask:
data(can.sig.go);
fpath <- can.sig.go
gs.list <- import.gs(fpath, Lowercase = 1, col.gene = 2, col.set = 3);
data(net.kegg)
netpath <- net.kegg
net <- import.net(netpath)
b0 <- benchmark (NET = net,
GS = gs.list,
echo=1, graph=TRUE, na.replace = 0, mask = ".", minN = 0,
coff.z = 1.965, coff.fdr = 0.1, Parallelize=2);
roc(b0, coff.z = 1.64);
## Not run:
## Benchmark and plot a number of networks on GO terms and KEGG pathways separately, using masks
b1 <- NULL;
for (mask in c("kegg_", "go_")) {
b1[[mask]] <- NULL;
for (file.net in c("netpath")) {
# a series of networks can be put here: c("netpath1", "netpath2", "netpath3")
net <- import.net(netpath, col.1 = 1, col.2 = 2, Lowercase = 1, echo = 1)
b1[[mask]][[file.net]] <- benchmark (NET = net, GS = gs.list,
gs.gene.col = 2, gs.group.col = 3, net.gene1.col = 1, net.gene2.col = 2,
echo=1, graph=FALSE, na.replace = 0, mask = mask, minN = 0, Parallelize=2);
}}
par(mfrow=c(2,1));
roc(b1[["kegg_"]], coff.z = 2.57,main="kegg_");
roc(b1[["go_"]], coff.z = 2.57,main="go_");
## End(Not run)
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