plot.referenceComparison {cTRAP} | R Documentation

## Plot data comparison

### 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")

[Package cTRAP version 1.10.1 Index]