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]