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
method includes gsea)
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().
Character: comparison method (spearman, pearson
or 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 method includes gsea and if input
is not a gene set
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.
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
details below)
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")
#> Loading data from expressionDrugSensitivityCorGDSC7.qs...
# Predict targeting drugs on a differential expression profile
predictTargetingDrugs(diffExprStat, gdsc)
#> Subsetting data based on 11396 intersecting genes (85% of the 13451 input genes)...
#> Comparing against 266 GDSC 7 compounds (983 cell lines) using 'spearman, pearson, gsea' (gene size of 150)...
#> Comparison performed in 3.23 secs
#> compound spearman_coef spearman_pvalue spearman_qvalue pearson_coef
#> 1: 1047 0.14532331 7.908329e-55 2.103616e-52 0.13098933
#> 2: 207 0.10447948 4.950795e-29 2.633823e-27 0.09295384
#> 3: 157 0.12935630 1.020442e-43 1.357188e-41 0.11655136
#> 4: 1091 0.08481582 1.194387e-19 1.512890e-18 0.08245367
#> 5: 110 0.11857369 5.806289e-37 5.148243e-35 0.11346259
#> ---
#> 262: 1133 -0.02817757 2.627306e-03 3.529613e-03 -0.02761751
#> 263: 1149 -0.01812593 5.299930e-02 6.210491e-02 -0.02690099
#> 264: 35 -0.02099097 2.503708e-02 3.069061e-02 -0.03075034
#> 265: 1029 -0.04152497 9.237123e-06 1.565016e-05 -0.04525941
#> 266: 1031 -0.08489029 1.109885e-19 1.476147e-18 -0.08808410
#> pearson_pvalue pearson_qvalue GSEA spearman_rank pearson_rank
#> 1: 8.577544e-45 2.281627e-42 0.0000000 1 1
#> 2: 2.693140e-23 1.023393e-21 0.2769544 5 7
#> 3: 9.209531e-36 1.224868e-33 0.0000000 2 2
#> 4: 1.184660e-18 2.100798e-17 0.3118949 20 14
#> 5: 5.716381e-34 5.068524e-32 0.0000000 3 3
#> ---
#> 262: 3.193570e-03 4.591836e-03 -0.2605952 263 261
#> 263: 4.079592e-03 5.772189e-03 -0.3444514 259 260
#> 264: 1.026803e-03 1.587963e-03 -0.3038956 261 263
#> 265: 1.341660e-06 2.949435e-06 -0.3298198 264 264
#> 266: 4.491968e-21 1.194863e-19 -0.3800912 266 266
#> GSEA_rank rankProduct_rank
#> 1: 130.5 1
#> 2: 9.0 2
#> 3: 130.5 3
#> 4: 3.0 4
#> 5: 130.5 5
#> ---
#> 262: 257.0 262
#> 263: 265.0 263
#> 264: 262.0 264
#> 265: 264.0 265
#> 266: 266.0 266