Analyse drug set enrichment
analyseDrugSetEnrichment(
sets,
stats,
col = NULL,
nperm = 10000,
maxSize = 500,
...,
keyColSets = NULL,
keyColStats = NULL
)Named list of characters: named sets containing compound
identifiers (obtain drug sets by running prepareDrugSets())
Named numeric vector or either a similarPerturbations or
a targetingDrugs object (obtained after running
rankSimilarPerturbations or
predictTargetingDrugs, respectively)
Character: name of the column to use for statistics (only required
if class of stats is either similarPerturbations or
targetingDrugs)
Number of permutations to do. Minimial possible nominal p-value is about 1/nperm
Maximal size of a gene set to test. All pathways above the threshold are excluded.
Arguments passed on to fgsea::fgseaSimple
minSizeMinimal size of a gene set to test. All pathways below the threshold are excluded.
scoreTypeThis parameter defines the GSEA score type. Possible options are ("std", "pos", "neg")
nprocIf not equal to zero sets BPPARAM to use nproc workers (default = 0).
gseaParamGSEA parameter value, all gene-level statis are raised to the power of `gseaParam` before calculation of GSEA enrichment scores.
BPPARAMParallelization parameter used in bplapply. Can be used to specify cluster to run. If not initialized explicitly or by setting `nproc` default value `bpparam()` is used.
Character: column from sets to compare with column
keyColStats from stats; automatically selected if NULL
Character: column from stats to compare with column
keyColSets from sets; automatically selected if NULL
Enrichment analysis based on GSEA
Other functions for drug set enrichment analysis:
loadDrugDescriptors(),
plotDrugSetEnrichment(),
prepareDrugSets()
descriptors <- loadDrugDescriptors()
#> compound_descriptors_NCI60_2D.qs not found: downloading data...
drugSets <- prepareDrugSets(descriptors)
# Analyse drug set enrichment in ranked targeting drugs for a differential
# expression profile
data("diffExprStat")
gdsc <- loadExpressionDrugSensitivityAssociation("GDSC")
#> expressionDrugSensitivityCorGDSC7.qs not found: downloading data...
#> Loading data from expressionDrugSensitivityCorGDSC7.qs...
predicted <- 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 3.1 secs
analyseDrugSetEnrichment(drugSets, predicted)
#> Matching compounds with those available in drug sets...
#> Ordering results by column 'rankProduct_rank'; to manually select column to order by, please set argument 'col'
#> Columns 'name' and 'name' were matched based on 56 common values; to manually select columns to compare, please set arguments starting with 'keyCol'
#> Performing enrichment analysis...
#> descriptor pval padj ES
#> 1: Small Rings: 4 0.0003085467 0.05090214 -0.6915988
#> 2: sp3-Atoms: 5 0.0003584658 0.05090214 -0.9037610
#> 3: Non-C/H Atoms: 11 0.0005410670 0.05122101 -0.7500000
#> 4: H-Donors: [6, 40] 0.0014800197 0.10508140 0.8880866
#> 5: Polar Surface Area: [123, 141] 0.0037672666 0.19105424 -0.6509521
#> ---
#> 280: Non-C/H Atoms: 7 0.9214334941 0.93127086 -0.2876703
#> 281: Electronegative Atoms: 7 0.9214334941 0.93127086 -0.2876703
#> 282: Small Rings: 3 0.9370152032 0.94366070 -0.2350154
#> 283: Total Molweight: [294, 312] 0.9776148970 0.97761490 -0.5107143
#> 284: Stereo Centers: 3 0.9776148970 0.97761490 -0.5107143
#> NES nMoreExtreme size leadingEdge
#> 1: -1.9319375 2 15 737754,752782,757441,1011,732517,759877,...
#> 2: -1.9399008 2 5 174939,737754,752782
#> 3: -1.8794823 4 9 299933,330507,727989,745750,732517,759877,...
#> 4: 1.9763189 2 4 125066,758612,759155,123127
#> 5: -1.7370475 35 12 174939,737754,752782,287459,63878,732517,...
#> ---
#> 280: -0.6500788 8021 6 706995
#> 281: -0.6500788 8021 6 706995
#> 282: -0.6467306 9059 14 299933,706995,683864,339555,606698,226080,...
#> 283: -0.6841599 4934 1 613327
#> 284: -0.6841599 4934 1 613327