rankSimilarPerturbations {cTRAP} | R Documentation |
Compare differential expression results against CMap perturbations.
rankSimilarPerturbations(diffExprGenes, perturbations, method = c("spearman", "pearson", "gsea"), geneSize = 150, cellLineMean = "auto", rankPerCellLine = FALSE)
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 |
|
method |
Character: comparison method ( |
geneSize |
Number: top and bottom number of differentially expressed
genes for gene set enrichment (only used if |
cellLineMean |
Boolean: add a column with the mean score across cell
lines? If |
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 |
Data table with correlation or GSEA results comparing differential expression values with those associated with CMap perturbations
Weighted connectivity scores (WTCS) are calculated when method
= "gsea"
(https://clue.io/connectopedia/cmap_algorithms).
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
# 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")