Predict targeting drugs

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
)

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)

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 loadExpressionDrugSensitivityAssociation().

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

isDrugActivityDirectlyProportionalToSensitivity

Boolean: are the values used for drug activity directly proportional to drug sensitivity? If NULL, the argument expressionDrugSensitivityCor must have a non-NULL value for attribute isDrugActivityDirectlyProportionalToSensitivity.

threads

Integer: number of parallel threads

chunkGiB

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 details below)

verbose

Boolean: print additional details?

Value

Data table with correlation and/or GSEA score results

Process data by chunks

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).

GSEA score

When method = "gsea", weighted connectivity scores (WTCS) are calculated (https://clue.io/connectopedia/cmap_algorithms).

See also

Other functions related with the prediction of targeting drugs: as.table.referenceComparison(), listExpressionDrugSensitivityAssociation(), loadExpressionDrugSensitivityAssociation(), plot.referenceComparison(), plotTargetingDrugsVSsimilarPerturbations()

Examples

# 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