| tni.plot.sre {RTN} | R Documentation |
This method plots the results of the subgroup regulon enrichment analysis in a heatmap. The rows of the heatmap represent enriched regulons, while the columns show the subgroups. The plotted values correspond to average regulon activity for a regulon in a subgroup. Enriched values can be marked.
tni.plot.sre(object, nGroupsEnriched = NULL, nTopEnriched = NULL,
colors = c("blue","white","red"), breaks = seq(-1.5, 1.5, 0.1),
markEnriched = TRUE, ...)
object |
A TNI-class object. |
nGroupsEnriched |
a filter to keep 'nGroupsEnriched' regulons; a single integer specifying how many subgroups a regulon has to be enriched for it to appear in the rows of the heatmap (it must be use either 'nGroupsEnriched' or 'nTopEnriched'). |
nTopEnriched |
a filter to keep 'nTopEnriched' regulons; a single integer specifying how many regulons will be shown for each group. The top regulons are chosen by significance (it must be use either 'nTopEnriched' or 'nGroupsEnriched'). |
colors |
a vector of color for the 'pheatmap'. |
breaks |
a numerical vector of breaks for the 'pheatmap'. |
markEnriched |
a single logical value. If TRUE, asterisks are added to cells of heatmap that were found to be significant. |
... |
parameters passed to 'pheatmap' for customization. |
A heatmap of the subgroup regulon enrichment results.
# load tniData
data(tniData)
## Not run:
# preprocessing
rtni <- tni.constructor(expData=tniData$expData,
regulatoryElements=c("PTTG1","E2F2","FOXM1","E2F3","RUNX2"),
rowAnnotation=tniData$rowAnnotation)
# permutation analysis (infers the reference/relevance network)
rtni <- tni.permutation(rtni)
# dpi filter (infers the transcriptional network)
rtni <- tni.dpi.filter(rtni)
#run GSEA2 analysis pipeline
rtni <- tni.gsea2(rtni)
# set sample groups
colAnnotation <- tni.get(rtni, "colAnnotation")
sampleGroups <- list(G1=colAnnotation$ID[1:60],
G2=colAnnotation$ID[61:90],
G3=colAnnotation$ID[91:120])
# run subgroup regulon enrichment analysis
rtni <- tni.sre(rtni, sampleGroups)
# plot results
tni.plot.sre(rtni)
## End(Not run)