Plot similar perturbations against predicted targeting drugs

Plot similar perturbations against predicted targeting drugs

plotTargetingDrugsVSsimilarPerturbations(
  targetingDrugs,
  similarPerturbations,
  column,
  labelBy = "pert_iname",
  quantileThreshold = 0.25,
  showAllScores = FALSE,
  keyColTargetingDrugs = NULL,
  keyColSimilarPerturbations = NULL
)

Arguments

targetingDrugs

targetingDrugs object

similarPerturbations

similarPerturbations object

column

Character: column to plot (must be available in both databases)

labelBy

Character: column in as.table(similarPerturbations) or as.table(targetingDrugs) to be used for labelling

quantileThreshold

Numeric: quantile (between 0 and 1) to highlight values of interest

showAllScores

Boolean: show all scores? If FALSE, only the best score per compound will be plotted

keyColTargetingDrugs

Character: column from targetingDrugs to compare with column keyColSimilarPerturbations from similarPerturbations; automatically selected if NULL

keyColSimilarPerturbations

Character: column from similarPerturbations to compare with column keyColTargetingDrugs from targetingDrugs; automatically selected if NULL

Value

ggplot2 plot

See also

Other functions related with the ranking of CMap perturbations: as.table.referenceComparison(), filterCMapMetadata(), getCMapConditions(), getCMapPerturbationTypes(), loadCMapData(), loadCMapZscores(), parseCMapID(), plot.perturbationChanges(), plot.referenceComparison(), prepareCMapPerturbations(), print.similarPerturbations(), rankSimilarPerturbations()

Other functions related with the prediction of targeting drugs: as.table.referenceComparison(), listExpressionDrugSensitivityAssociation(), loadExpressionDrugSensitivityAssociation(), plot.referenceComparison(), predictTargetingDrugs()

Examples

# Rank similarity against CMap compound perturbations
similarPerts <- rankSimilarPerturbations(diffExprStat,
                                         cmapPerturbationsCompounds)
#> Subsetting data based on 8790 intersecting genes (65% of the 13451 input genes)...
#> Comparing against 22 CMap perturbations (2 cell lines) using 'spearman, pearson, gsea' (gene size of 150)...
#> Comparison performed in 1.77 secs

# Predict targeting drugs
gdsc <- loadExpressionDrugSensitivityAssociation("GDSC 7")
#> Loading data from expressionDrugSensitivityCorGDSC7.qs...
predicted <- predictTargetingDrugs(diffExprStat, gdsc)
#> Subsetting data based on 11396 intersecting genes (85% of the 13451 input genes)...
#> Comparing against 266 GDSC 7 compounds (983 cell lines) using 'spearman, pearson, gsea' (gene size of 150)...
#> Comparison performed in 2.58 secs

plotTargetingDrugsVSsimilarPerturbations(predicted, similarPerts,
                                         "spearman_rank")
#> Columns 'name' and 'pubchem_cid' were matched based on 0 common values; to manually select columns to compare, please set arguments starting with 'keyCol'
#> Warning: Ignoring empty aesthetic: `label`.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.