EnrichIntersect (version 0.7)

enrichment: Plot enrichment map

Description

Plot enrichment map through a vector (matrix) of scores and a self-defined set that summarizes a few groups of the names (rownames) of the vector (matrix)

Usage

enrichment(
  x,
  custom.set,
  alpha = 0,
  normalize = TRUE,
  permute.n = 100,
  padj.method = "none",
  pvalue.cutoff = 0.05,
  angle = 45,
  ...
)

Value

Return a list including a matrix of (normalized) enrichment score, a matrix of corresponding p-value and ggplot object:

• S - a matrix of calculated enrichment scores.

• pvalue - a matrix of p-values using permuation test for the calculated enrichment scores.

• g - a ggplot object for visualising the results of an enrichment analysis.

Arguments

x

a vector (matrix) of scores to be enriched

custom.set

a self-defined set that summarizes a few groups of the names (rownames) of x

alpha

exponent weight of the score of ordered features. Default is 0 for calculating enrichment score via classic Kolmogorov-Smirnov statistic

normalize

logic value to determine if normalizing enrichment scores, accounting for custom set size. Default is TRUE

permute.n

number of custom-set permutations for significance testing. Default is 100

padj.method

correction method, one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". Default is "none"

pvalue.cutoff

a cutoff for both unadjusted and adjusted p-value to mark significantly enriched classes. Default is 0.05

angle

angle of rotating x-axis labels. Default is 45

...

other arguments

References

Reimand J, Isserlin R, Voisin V, et al (2019). Pathway enrichment analysis and visualization of omics data using g:profiler, gsea, cytoscape and enrichmentmap. Nature protocols, 14:482–517.

Examples

# Data set 'cancers_drug_groups' is a list including a score dataframe with 147 drugs as rows
# and 19 cancer types as columns, and a dataframe with 9 self-defined drug groups (1st column)
# of the 147 drugs (2nd column).
data(cancers_drug_groups, package = "EnrichIntersect")

x <- cancers_drug_groups$score
custom.set <- cancers_drug_groups$custom.set
set.seed(123)
enrich <- enrichment(x, custom.set, permute.n = 5)