predictTargetingDrugs {cTRAP} | R Documentation |
Identify compounds that may target the phenotype associated with a user-provided differential expression profile by comparing such against a correlation matrix of gene expression and drug sensitivity.
predictTargetingDrugs( input, expressionDrugSensitivityCor, method = c("spearman", "pearson", "gsea"), geneSize = 150, isDrugActivityDirectlyProportionalToSensitivity = NULL, threads = 1, chunkGiB = 1, verbose = FALSE )
input |
|
expressionDrugSensitivityCor |
Matrix or character: correlation matrix
of gene expression (rows) and drug sensitivity (columns) across cell lines
or path to file containing such data; see
|
method |
Character: comparison method ( |
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 |
isDrugActivityDirectlyProportionalToSensitivity |
Boolean: are the
values used for drug activity directly proportional to drug sensitivity?
If |
threads |
Integer: number of parallel threads |
chunkGiB |
Numeric: if second argument is a path to an HDF5 file
( |
verbose |
Boolean: print additional details? |
Data table with correlation and/or GSEA score results
If a file path to a valid HDF5 (.h5
) file is provided instead of a
data matrix, that file can be loaded and processed in chunks of size
chunkGiB
, resulting in decreased peak memory usage.
The default value of 1 GiB (1 GiB = 1024^3 bytes) allows loading chunks of ~10000 columns and
14000 rows (10000 * 14000 * 8 bytes / 1024^3 = 1.04 GiB
).
When method = "gsea"
, weighted connectivity scores (WTCS) are
calculated (https://clue.io/connectopedia/cmap_algorithms).
Other functions related with the prediction of targeting drugs:
as.table.referenceComparison()
,
listExpressionDrugSensitivityAssociation()
,
loadExpressionDrugSensitivityAssociation()
,
plot.referenceComparison()
,
plotTargetingDrugsVSsimilarPerturbations()
# Example of a differential expression profile data("diffExprStat") # Load expression and drug sensitivity association derived from GDSC data gdsc <- loadExpressionDrugSensitivityAssociation("GDSC 7") # Predict targeting drugs on a differential expression profile predictTargetingDrugs(diffExprStat, gdsc)