Prepare CMap perturbation data
prepareCMapPerturbations(
metadata,
zscores,
geneInfo,
compoundInfo = NULL,
...,
loadZscores = FALSE
)
Data frame (CMap metadata) or character (respective filepath to load data from file)
Data frame (GCTX z-scores) or character (respective filepath to load data from file)
Data frame (CMap gene info) or character (respective filepath to load data from file)
Data frame (CMap compound info) or character (respective filepath to load data from file)
Arguments passed on to filterCMapMetadata
cellLine
Character: cell line (if NULL
, all values are loaded)
timepoint
Character: timepoint (if NULL
, all values are loaded)
dosage
Character: dosage (if NULL
, all values are loaded)
perturbationType
Character: type of perturbation (if NULL
, all
perturbation types are loaded)
Boolean: load matrix of perturbation z-scores? Not
recommended in systems with less than 30GB of RAM; if FALSE
,
downstream functions will load and process the file directly chunk by
chunk, resulting in a lower memory footprint
CMap perturbation data attributes and filename
Other functions related with the ranking of CMap perturbations:
as.table.referenceComparison()
,
filterCMapMetadata()
,
getCMapConditions()
,
getCMapPerturbationTypes()
,
loadCMapData()
,
loadCMapZscores()
,
parseCMapID()
,
plot.perturbationChanges()
,
plot.referenceComparison()
,
plotTargetingDrugsVSsimilarPerturbations()
,
print.similarPerturbations()
,
rankSimilarPerturbations()
metadata <- loadCMapData("cmapMetadata.txt", "metadata")
#> Loading CMap metadata from cmapMetadata.txt...
metadata <- filterCMapMetadata(metadata, cellLine="HepG2")
if (FALSE) { # \dontrun{
prepareCMapPerturbations(metadata, "cmapZscores.gctx", "cmapGeneInfo.txt")
} # }