plot.referenceComparison: Plot data comparison

In cTRAP: Identification of candidate causal perturbations from differential gene expression data

Description

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.

Usage

## 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
)

Arguments

x	referenceComparison object: obtained after running rankSimilarPerturbations() or predictTargetingDrugs()
element	Character: identifier in the first column of x
method	Character: method to plot results; choose between spearman, pearson, gsea or rankProduct (the last one is only available if element = NULL)
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 element = NULL
showMetadata	Boolean: show available metadata information instead of identifiers (if available)? Only used if element = NULL
plotNonRankedPerturbations	Boolean: plot non-ranked data in grey? Only used if element = NULL
alpha	Numeric: transparency; only used if element = NULL
genes	Character: when plotting gene set enrichment analysis (GSEA), plot most up-regulated genes (genes = "top"), most down-regulated genes (genes = "bottom") or both (genes = "both"); only used if method = "gsea" and geneset = NULL
...	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 NULL, the default title depends on the context; ignored when plotting multiple perturbations)

Value

Plot illustrating the reference comparison

See Also

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()

Examples

# 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")

cTRAP documentation built on Nov. 8, 2020, 10:58 p.m.