Description Usage Arguments Value Examples
View source: R/visualisation.R
Draw network of enriched functional annotation pairs as a heatmap
1 2 | draw_heatmap(graph, adjMethod = NULL, xlab = "downstream",
ylab = "upstream", colPal = NULL)
|
graph |
The output of either the pafway or pafway_edge_weight functions |
adjMethod |
The method for correcting for multiple hypotheses. This can be any method that is acceptable to the p.adjust function in the stats package: "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr" or "none". If this is NULL, then no adjustment will be made. |
xlab |
The label for the x-axis of the heatmap |
ylab |
The label for the y-axis of the heatmap |
colPal |
The color palette of the heatmap |
A matrix that has the same number of rows and columns as length(GOtypes). This will contain p-values.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | nodes=paste("node", c(1:300))
set.seed(123)
randomGO=c("A", "B", "C", "D", "E", "F", "G", "H", "I",
"J", "K", "L", "M", "N")[sample(c(1:14), 300, replace=TRUE)]
names(randomGO)=nodes
edgesRandom=sapply(c(1:1000), function(i){
nodes[sample(300, 2)]
})
getBinomPvalueRandom1=pafway(randomGO, t(edgesRandom), unique(randomGO))
draw_heatmap(getBinomPvalueRandom1)
colPal1=c(colorRampPalette(c("red3", "lightpink", "white", "white"))(20),
colorRampPalette(c("white", "white", "lightgreen", "darkgreen"))(20))
draw_heatmap(getBinomPvalueRandom1, adjMethod="bonferroni", xlab="Downstream",
ylab="Upstream", colPal=colPal1)
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