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 )
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
Matrix or character: correlation matrix
of gene expression (rows) and drug sensitivity (columns) across cell lines
or path to file containing such data; see
Character: comparison method (
gsea; multiple methods may be selected at once)
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
gsea and if
is not a gene set
Boolean: are the
values used for drug activity directly proportional to drug sensitivity?
NULL, the argument
expressionDrugSensitivityCor must have
NULL value for attribute
Integer: number of parallel threads
Numeric: if second argument is a path to an HDF5 file
.h5 extension), that file is loaded and processed in chunks of a
given size in gibibytes (GiB); lower values decrease peak RAM usage (see
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).
method = "gsea", weighted connectivity scores (WTCS) are
Other functions related with the prediction of targeting drugs:
# Example of a differential expression profile data("diffExprStat") # Load expression and drug sensitivity association derived from GDSC data gdsc <- loadExpressionDrugSensitivityAssociation("GDSC 7") #> Loading data from expressionDrugSensitivityCorGDSC7.qs... #> Error in qread(file): Malformed compress block: compressed size > compress bound # Predict targeting drugs on a differential expression profile predictTargetingDrugs(diffExprStat, gdsc) #> Error in predictTargetingDrugs(diffExprStat, gdsc): object 'gdsc' not found