rankSimilarPerturbations {cTRAP} | R Documentation

## Rank CMap perturbations' similarity to a differential expression profile

### Description

Compare differential expression results against CMap perturbations.

### Usage

    rankSimilarPerturbations(diffExprGenes, perturbations,
      method = c("spearman", "pearson", "gsea"), geneSize = 150,
      cellLineMean = "auto", rankPerCellLine = FALSE)

### Arguments

`diffExprGenes` | Numeric: named vector of differentially expressed genes whose names are gene identifiers and respective values are a statistic that represents significance and magnitude of differentially expressed genes (e.g. t-statistics)
`perturbations` | `perturbationChanges` object: CMap perturbations (check `prepareCMapPerturbations`)
`method` | Character: comparison method (`spearman`, `pearson` or `gsea`; multiple methods may be selected at once)
`geneSize` | Number: top and bottom number of differentially expressed genes for gene set enrichment (only used if `method = gsea`)
`cellLineMean` | Boolean: add a column with the mean score across cell lines? If `cellLineMean = "auto"` (default) the mean score will be added if more than one cell line is available
`rankPerCellLine` | Boolean: when ranking results, also rank them based on individual cell lines instead of only focusing on the mean score across cell lines; if `cellLineMean = FALSE`, individual cell line conditions are always ranked

### Value

Data table with correlation or GSEA results comparing differential expression values with those associated with CMap perturbations

### GSEA score

Weighted connectivity scores (WTCS) are calculated when `method = "gsea"` (https://clue.io/connectopedia/cmap_algorithms).

### See Also

Other functions related with the ranking of CMap perturbations: `[.perturbationChanges`, `as.table.similarPerturbations`, `dim.perturbationChanges`, `dimnames.perturbationChanges`, `filterCMapMetadata`, `getCMapConditions`, `getCMapPerturbationTypes`, `loadCMapData`, `loadCMapZscores`, `parseCMapID`, `plot.perturbationChanges`, `plot.referenceComparison`, `plotTargetingDrugsVSsimilarPerturbations`, `prepareCMapPerturbations`, `print.similarPerturbations`

### Examples

    # Example of a differential expression profile
    data("diffExprStat")

    ## Not run:
    # Download and load CMap perturbations to compare with
    cellLine <- c("HepG2", "HUH7")
    cmapMetadataCompounds <- filterCMapMetadata(
        "cmapMetadata.txt", cellLine=cellLine, timepoint="24 h",
        dosage="5 \u00B5M", perturbationType="Compound")

    cmapPerturbationsCompounds <- prepareCMapPerturbations(
        cmapMetadataCompounds, "cmapZscores.gctx", "cmapGeneInfo.txt",
        "cmapCompoundInfo_drugs.txt", loadZscores=TRUE)

    ## End(Not run)
    perturbations <- cmapPerturbationsCompounds

    # Rank similar CMap perturbations (by default, Spearman's and Pearson's
    # correlation are used, as well as GSEA with the top and bottom 150 genes of
    # the differential expression profile used as reference)
    rankSimilarPerturbations(diffExprStat, perturbations)

    # Rank similar CMap perturbations using only Spearman's correlation
    rankSimilarPerturbations(diffExprStat, perturbations, method="spearman")

[Package cTRAP version 1.5.0 Index]