Operations on a perturbationChanges object

# S3 method for perturbationChanges
plot(
  x,
  perturbation,
  input,
  method = c("spearman", "pearson", "gsea"),
  geneSize = 150,
  genes = c("both", "top", "bottom"),
  ...,
  title = NULL
)

# S3 method for perturbationChanges
[(x, i, j, drop = FALSE, ...)

# S3 method for perturbationChanges
dim(x)

# S3 method for perturbationChanges
dimnames(x)

Arguments

x

perturbationChanges object

perturbation

Character (perturbation identifier) or a similarPerturbations table (from which the respective perturbation identifiers are retrieved)

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)

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

genes

Character: when plotting gene set enrichment analysis (GSEA), plot most up-regulated genes (genes = "top"), most down-regulated genes (genes = "bottom") or both (genes = "both"); only used if method = "gsea" and geneset = NULL

...

Extra arguments

title

Character: plot title (if NULL, the default title depends on the context; ignored when plotting multiple perturbations)

i, j

Character or numeric indexes specifying elements to extract

drop

Boolean: coerce result to the lowest possible dimension?

Value

Subset, plot or return dimensions or names of a

perturbationChanges object

Examples

data("diffExprStat")
data("cmapPerturbationsKD")

compareKD <- rankSimilarPerturbations(diffExprStat, cmapPerturbationsKD)
#> Subsetting data based on 8790 intersecting genes (65% of the 13451 input genes)...
#> Comparing against 26 comparisons (1 cell line) using 'spearman, pearson, gsea' (gene size of 150)...
#> Comparison performed in 0.65 secs
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")