plotTargetingDrugsVSsimilarPerturbations {cTRAP} | R Documentation

## Plot similar perturbations against predicted targeting drugs

### Description

Plot similar perturbations against predicted targeting drugs

### Usage

    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)

    # Predict targeting drugs
    gdsc <- loadExpressionDrugSensitivityAssociation("GDSC 7")
    predicted <- predictTargetingDrugs(diffExprStat, gdsc)

    plotTargetingDrugsVSsimilarPerturbations(predicted, similarPerts,
                                             "spearman_rank")

[Package cTRAP version 1.12.0 Index]