plot.perturbationChanges {cTRAP} | R Documentation |
perturbationChanges
objectOperations on a perturbationChanges
object
## S3 method for class 'perturbationChanges' plot( x, perturbation, input, method = c("spearman", "pearson", "gsea"), geneSize = 150, genes = c("both", "top", "bottom"), ..., title = NULL ) ## S3 method for class 'perturbationChanges' x[i, j, drop = FALSE, ...] ## S3 method for class 'perturbationChanges' dim(x) ## S3 method for class 'perturbationChanges' dimnames(x)
x |
|
perturbation |
Character (perturbation identifier) or a
|
input |
|
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 |
genes |
Character: when plotting gene set enrichment analysis (GSEA),
plot most up-regulated genes ( |
... |
Extra arguments |
title |
Character: plot title (if |
i, j |
Character or numeric indexes specifying elements to extract |
drop |
Boolean: coerce result to the lowest possible dimension? |
Subset, plot or return dimensions or names of a
perturbationChanges
object
Other functions related with the ranking of CMap perturbations:
as.table.referenceComparison()
,
filterCMapMetadata()
,
getCMapConditions()
,
getCMapPerturbationTypes()
,
loadCMapData()
,
loadCMapZscores()
,
parseCMapID()
,
plot.referenceComparison()
,
plotTargetingDrugsVSsimilarPerturbations()
,
prepareCMapPerturbations()
,
print.similarPerturbations()
,
rankSimilarPerturbations()
data("diffExprStat") data("cmapPerturbationsKD") compareKD <- rankSimilarPerturbations(diffExprStat, cmapPerturbationsKD) EIF4G1knockdown <- grep("EIF4G1", compareKD[[1]], value=TRUE) plot(cmapPerturbationsKD, EIF4G1knockdown, diffExprStat, method="spearman") plot(cmapPerturbationsKD, EIF4G1knockdown, diffExprStat, method="pearson") plot(cmapPerturbationsKD, EIF4G1knockdown, diffExprStat, method="gsea") data("cmapPerturbationsCompounds") pert <- "CVD001_HEPG2_24H:BRD-A14014306-001-01-1:4.1" plot(cmapPerturbationsCompounds, pert, diffExprStat, method="spearman") plot(cmapPerturbationsCompounds, pert, diffExprStat, method="pearson") plot(cmapPerturbationsCompounds, pert, diffExprStat, method="gsea") # Multiple cell line perturbations pert <- "CVD001_24H:BRD-A14014306-001-01-1:4.1" plot(cmapPerturbationsCompounds, pert, diffExprStat, method="spearman") plot(cmapPerturbationsCompounds, pert, diffExprStat, method="pearson") plot(cmapPerturbationsCompounds, pert, diffExprStat, method="gsea")