plot.referenceComparison {cTRAP} | R Documentation |
If element = NULL
, comparison is plotted based on all elements.
Otherwise, show scatter or GSEA plots for a single element compared with
previously given differential expression results.
## S3 method for class 'referenceComparison' plot( x, element = NULL, method = c("spearman", "pearson", "gsea", "rankProduct"), n = c(3, 3), showMetadata = TRUE, plotNonRankedPerturbations = FALSE, alpha = 0.3, genes = c("both", "top", "bottom"), ..., zscores = NULL, title = NULL )
x |
|
element |
Character: identifier in the first column of |
method |
Character: method to plot results; choose between
|
n |
Numeric: number of top and bottom genes to label (if a vector of two
numbers is given, the first and second numbers will be used as the number
of top and bottom genes to label, respectively); only used if
|
showMetadata |
Boolean: show available metadata information instead of
identifiers (if available)? Only used if |
plotNonRankedPerturbations |
Boolean: plot non-ranked data in grey? Only
used if |
alpha |
Numeric: transparency; only used if |
genes |
Character: when plotting gene set enrichment analysis (GSEA),
plot most up-regulated genes ( |
... |
Extra arguments currently not used |
zscores |
Data frame (GCTX z-scores) or character (respective filepath to load data from file) |
title |
Character: plot title (if |
Plot illustrating the reference comparison
Other functions related with the ranking of CMap perturbations:
as.table.referenceComparison()
,
filterCMapMetadata()
,
getCMapConditions()
,
getCMapPerturbationTypes()
,
loadCMapData()
,
loadCMapZscores()
,
parseCMapID()
,
plot.perturbationChanges()
,
plotTargetingDrugsVSsimilarPerturbations()
,
prepareCMapPerturbations()
,
print.similarPerturbations()
,
rankSimilarPerturbations()
Other functions related with the prediction of targeting drugs:
as.table.referenceComparison()
,
listExpressionDrugSensitivityAssociation()
,
loadExpressionDrugSensitivityAssociation()
,
plotTargetingDrugsVSsimilarPerturbations()
,
predictTargetingDrugs()
# Example of a differential expression profile data("diffExprStat") ## Not run: # Download and load CMap perturbations to compare with cellLine <- "HepG2" cmapMetadataKD <- filterCMapMetadata( "cmapMetadata.txt", cellLine=cellLine, perturbationType="Consensus signature from shRNAs targeting the same gene") cmapPerturbationsKD <- prepareCMapPerturbations( cmapMetadataKD, "cmapZscores.gctx", "cmapGeneInfo.txt", loadZscores=TRUE) ## End(Not run) # Rank similar CMap perturbations compareKD <- rankSimilarPerturbations(diffExprStat, cmapPerturbationsKD) # Plot ranked list of CMap perturbations plot(compareKD, method="spearman") plot(compareKD, method="spearman", n=c(7, 3)) plot(compareKD, method="pearson") plot(compareKD, method="gsea") # Plot results for a single perturbation pert <- compareKD[[1, 1]] plot(compareKD, pert, method="spearman", zscores=cmapPerturbationsKD) plot(compareKD, pert, method="pearson", zscores=cmapPerturbationsKD) plot(compareKD, pert, method="gsea", zscores=cmapPerturbationsKD) # Predict targeting drugs based on a given differential expression profile gdsc <- loadExpressionDrugSensitivityAssociation("GDSC 7") predicted <- predictTargetingDrugs(diffExprStat, gdsc) # Plot ranked list of targeting drugs plot(predicted, method="spearman") plot(predicted, method="spearman", n=c(7, 3)) plot(predicted, method="pearson") plot(predicted, method="gsea") # Plot results for a single targeting drug drug <- predicted$compound[[4]] plot(predicted, drug, method="spearman") plot(predicted, drug, method="pearson") plot(predicted, drug, method="gsea")