rankSimilarPerturbations: Rank CMap perturbations' similarity to a differential...

In cTRAP: Identification of candidate causal perturbations from differential gene expression data

Description

Compare differential expression results against CMap perturbations.

Usage

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

Arguments

input	Named numeric 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); or character of gene symbols composing a gene set that is tested for enrichment in reference data (only used if method includes gsea)
perturbations	perturbationChanges object: CMap perturbations (check prepareCMapPerturbations)
method	Character: comparison method (spearman, pearson or gsea; multiple methods may be selected at once)
geneSize	Numeric: number of top up-/down-regulated genes to use as gene sets to test for enrichment in reference data; if a 2-length numeric vector, the first index is the number of top up-regulated genes and the second index is the number of down-regulated genes used to create gene sets; only used if method includes gsea and if input is not a gene set
cellLineMean	Boolean: add a column with the mean score across cell lines? If cellLineMean = "auto" (default), the mean score will be added when data for more than one cell line is available.
rankPerCellLine	Boolean: rank results based on both individual cell lines and mean scores across cell lines (TRUE) or based on mean scores alone (FALSE)? 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: as.table.referenceComparison(), 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")

cTRAP documentation built on Nov. 8, 2020, 10:58 p.m.