Package 'enrichplot'

Title: Visualization of Functional Enrichment Result
Description: The 'enrichplot' package provides visualization methods for interpreting functional enrichment results from ORA or GSEA analyses. It is designed to work with the 'clusterProfiler' ecosystem and builds on 'ggplot2' for flexible and extensible graphics.
Authors: Guangchuang Yu [aut, cre] (ORCID: <https://orcid.org/0000-0002-6485-8781>)
Maintainer: Guangchuang Yu <[email protected]>
License: Artistic-2.0
Version: 1.99.8
Built: 2026-10-09 16:31:18 UTC
Source: https://github.com/bioc/enrichplot

Help Index

Convert compareClusterResult to data frame

Description

Convert compareClusterResult to data frame

Usage

## S3 method for class 'compareClusterResult'
as.data.frame(x, ...)

Arguments

x

compareClusterResult object
...

additional parameters

Value

data frame

Plotting utility functions for enrichplot package

Description

This file contains plotting and visualization helper functions for enrichplot Automatically split barplot or dotplot into several facets

Usage

autofacet(by = "row", scales = "free", levels = NULL)

Arguments

by

one of 'row' or 'column'
scales

whether 'fixed' or 'free'
levels

set facet levels

Value

a ggplot object

barplot

Description

Barplot of enrichResult

Usage

## S3 method for class 'enrichResult'
barplot(
  height,
  x = "Count",
  color = "p.adjust",
  showCategory = 8,
  font.size = 12,
  title = "",
  label_format = 30,
  ...
)

Arguments

height

enrichResult object
x

one of 'Count' and 'GeneRatio'
color

one of 'pvalue', 'p.adjust' and 'qvalue'
showCategory

number of categories to display or a vector of terms.
font.size

font size
title

plot title
label_format

a numeric value sets wrap length, alternatively a custom function to format axis labels. by default wraps names longer than 30 characters
...

additional parameters

Details

Barplot of enrichResult

Value

ggplot object

Examples

## Not run: 
library(DOSE)
data(geneList)
de <- names(geneList)[1:100]
x <- enrichDO(de)
barplot(x)
# use `showCategory` to select the displayed terms. It can be a number of a vector of terms.
barplot(x, showCategory = 10)
categories <- c("urinary bladder cancer", "bronchiolitis obliterans",
               "aortic aneurysm", "esophageal cancer")
barplot(x, showCategory = categories)

## End(Not run)

Create color palette for continuous data

Description

Create color palette for continuous data

Usage

color_palette(colors)

Arguments

colors

colors of length >=2

Value

color vector

Author(s)

guangchuang yu

Examples

color_palette(c("red", "yellow", "green"))

consensusmap

Description

Multi-context mechanism consensus map

Usage

consensusmap(
  x,
  fill_var = c("NES", "delta_NES"),
  size_var = c("rewiring_score", "leading_edge_overlap"),
  include_rewiring = TRUE,
  label = c("mechanism_class", "rewiring_score", "none"),
  reference = NULL,
  thresholds = list(rewire = 0.5, nes = 0.2),
  ...
)

## S4 method for signature 'mnseaResult'
consensusmap(
  x,
  fill_var = c("NES", "delta_NES"),
  size_var = c("rewiring_score", "leading_edge_overlap"),
  include_rewiring = TRUE,
  label = c("mechanism_class", "rewiring_score", "none"),
  reference = NULL,
  thresholds = list(rewire = 0.5, nes = 0.2),
  ...
)

## S4 method for signature 'nseaResult'
consensusmap(
  x,
  fill_var = c("NES", "delta_NES"),
  size_var = c("rewiring_score", "leading_edge_overlap"),
  include_rewiring = TRUE,
  label = c("mechanism_class", "rewiring_score", "none"),
  reference = NULL,
  thresholds = list(rewire = 0.5, nes = 0.2),
  ...
)

## S4 method for signature 'list'
consensusmap(
  x,
  fill_var = c("NES", "delta_NES"),
  size_var = c("rewiring_score", "leading_edge_overlap"),
  include_rewiring = TRUE,
  label = c("mechanism_class", "rewiring_score", "none"),
  reference = NULL,
  thresholds = list(rewire = 0.5, nes = 0.2),
  ...
)

Arguments

x

A mnseaResult, nseaResult, or a named list of results.
fill_var

One of "NES" or "delta_NES"; controls tile fill.
size_var

One of "rewiring_score" or "leading_edge_overlap"; controls point size.
include_rewiring

Logical; whether to draw topology-consistency points.
label

One of "mechanism_class", "rewiring_score", or "none".
reference

An optional reference result used to compute delta_NES. For list input, the first element is used as the default reference.
thresholds

Optional named list with rewire and nes numeric thresholds used to classify mechanism states.
...

Additional parameters passed to plot methods.

Details

consensusmap() creates a pathway by context heatmap. Tile fill represents enrichment strength (NES or delta_NES) and point size represents topology consistency (rewiring_score or leading_edge_overlap). Mechanism class is shown as tile text and point color.

Value

A ggplot object.

dotplot

Description

Dot plot for enrichment result

Usage

dotplot(object, ...)

## S4 method for signature 'enrichResult'
dotplot(
  object,
  x = "GeneRatio",
  color = "p.adjust",
  showCategory = 10,
  size = NULL,
  split = NULL,
  font.size = 12,
  title = "",
  orderBy = "x",
  label_format = 30,
  ...
)

## S4 method for signature 'gseaResult'
dotplot(
  object,
  x = "GeneRatio",
  color = "p.adjust",
  showCategory = 10,
  size = NULL,
  split = NULL,
  font.size = 12,
  title = "",
  orderBy = "x",
  label_format = 30,
  ...
)

## S4 method for signature 'mnseaResult'
dotplot(
  object,
  x = "share",
  color = "contribution",
  showCategory = 10,
  size = NULL,
  split = NULL,
  font.size = 12,
  title = "",
  orderBy = "x",
  label_format = 30,
  layer = NULL,
  ...
)

## S4 method for signature 'compareClusterResult'
dotplot(
  object,
  x = "Cluster",
  color = "p.adjust",
  showCategory = 5,
  split = NULL,
  font.size = 12,
  title = "",
  by = "geneRatio",
  size = NULL,
  includeAll = TRUE,
  label_format = 30,
  ...
)

## S4 method for signature 'enrichResultList'
dotplot(
  object,
  x = "GeneRatio",
  color = "p.adjust",
  showCategory = 10,
  size = NULL,
  split = NULL,
  font.size = 12,
  title = "",
  orderBy = "x",
  label_format = 30,
  ...
)

## S4 method for signature 'gseaResultList'
dotplot(
  object,
  x = "GeneRatio",
  color = "p.adjust",
  showCategory = 10,
  size = NULL,
  split = NULL,
  font.size = 12,
  title = "",
  orderBy = "x",
  label_format = 30,
  ...
)

dotplot.enrichResult(
  object,
  x = "geneRatio",
  color = "p.adjust",
  showCategory = 10,
  size = NULL,
  split = NULL,
  font.size = 12,
  title = "",
  orderBy = "x",
  label_format = 30,
  decreasing = TRUE
)

dotplot.compareClusterResult(
  object,
  x = "Cluster",
  colorBy = "p.adjust",
  showCategory = 5,
  by = "geneRatio",
  size = "geneRatio",
  split = NULL,
  includeAll = TRUE,
  font.size = 12,
  title = "",
  label_format = 30,
  group = FALSE,
  shape = FALSE,
  facet = NULL,
  strip_width = 15
)

Arguments

object

compareClusterResult object
...

additional parameters.
x

variable for x-axis, one of 'GeneRatio' and 'Count'
color

variable used to color enriched terms, e.g. 'pvalue', 'p.adjust' or 'qvalue'
showCategory

number of categories to display or a vector of terms.
size

variable used to scale the sizes of categories, one of "geneRatio", "Percentage" and "count". When x is "GeneRatio", the size defaults to "count" (number of genes); pass size = "GeneRatio" to scale point size by the enrichment gene ratio instead.
split

apply showCategory to each category specified by the 'split', e.g., "ONTOLOGY", "category" and "intersect". Default is NULL and do nothing
font.size

font size
title

figure title
orderBy

The order of the Y-axis
label_format

a numeric value sets wrap length, alternatively a custom function to format axis labels. by default wraps names longer than 30 characters
layer

optional layer or layers to retain for mnseaResult.
by

one of "geneRatio", "Percentage" and "count"
includeAll

logical value
decreasing

logical. Should the orderBy order be increasing or decreasing?
colorBy

variable used to color enriched terms, e.g. 'pvalue', 'p.adjust' or 'qvalue'
group

a logical value, whether to connect the nodes of the same group with wires.
shape

a logical value, whether to use nodes of different shapes to distinguish the group it belongs to
facet

apply facet_grid to the plot by specified variable, e.g., "ONTOLOGY", "category" and "intersect".
strip_width

width of strip text (facet label).

Value

plot.

Author(s)

Guangchuang Yu

Examples

## Not run: 
    library(DOSE)
    data(geneList)
    de <- names(geneList)[1:100]
    x <- enrichDO(de)
    dotplot(x)
    # use `showCategory` to select the displayed terms. It can be a number of a vector of terms.
    dotplot(x, showCategory = 10)
    categories <- c("pre-malignant neoplasm", "intestinal disease",
                   "breast ductal carcinoma", "non-small cell lung carcinoma")
    dotplot(x, showCategory = categories)
    # It can also graph compareClusterResult
    data(gcSample)
    library(clusterProfiler)
    library(DOSE)
    library(org.Hs.eg.db)
    data(gcSample)
    xx <- compareCluster(gcSample, fun="enrichGO", OrgDb="org.Hs.eg.db")
    xx2 <- pairwise_termsim(xx)
    library(ggstar)
    dotplot(xx2)
    dotplot(xx2, shape = TRUE)
    dotplot(xx2, group = TRUE)
    dotplot(xx2, x = "GeneRatio", group = TRUE, size = "count")

## End(Not run)

dotplot2

Description

compare two clusters in the compareClusterResult object

Usage

dotplot2(object, x = "FoldEnrichment", vars = NULL, label = "auto", ...)

Arguments

object

a compareClusterResult object
x

selected variable to visualize in x-axis
vars

selected Clusters to be compared, only length of two is supported
label

to label the Clusters in the plot, should be a named vector
...

additional parameters passed to dotplot

Value

a ggplot object

Author(s)

Guangchuang Yu

emapplot

Description

Enrichment Map for enrichment result of over-representation test or gene set enrichment analysis

Usage

emapplot(
  x,
  layout = igraph::layout_with_kk,
  coords = NULL,
  showCategory = 30,
  color = "p.adjust",
  size_category = 1,
  min_edge = 0.2,
  color_edge = "grey",
  size_edge = 0.5,
  group = NULL,
  group_legend = TRUE,
  node_label = "category",
  node_label_size = 5,
  pie = "equal",
  layer = NULL,
  label_format = 30,
  clusterFunction = stats::kmeans,
  nWords = 4,
  nCluster = NULL,
  show_category_size_legend = TRUE,
  edge_filter = "threshold",
  top_k = 5,
  target_density = 0.1,
  edge_diagnostic = FALSE,
  ...
)

## S4 method for signature 'enrichResult'
emapplot(
  x,
  layout = igraph::layout_with_kk,
  coords = NULL,
  showCategory = 30,
  color = "p.adjust",
  size_category = 1,
  min_edge = 0.2,
  color_edge = "grey",
  size_edge = 0.5,
  group = NULL,
  group_legend = TRUE,
  node_label = "category",
  node_label_size = 5,
  pie = "equal",
  layer = NULL,
  label_format = 30,
  clusterFunction = stats::kmeans,
  nWords = 4,
  nCluster = NULL,
  show_category_size_legend = TRUE,
  edge_filter = "threshold",
  top_k = 5,
  target_density = 0.1,
  edge_diagnostic = FALSE,
  ...
)

## S4 method for signature 'gseaResult'
emapplot(
  x,
  layout = igraph::layout_with_kk,
  coords = NULL,
  showCategory = 30,
  color = "p.adjust",
  size_category = 1,
  min_edge = 0.2,
  color_edge = "grey",
  size_edge = 0.5,
  group = NULL,
  group_legend = TRUE,
  node_label = "category",
  node_label_size = 5,
  pie = "equal",
  layer = NULL,
  label_format = 30,
  clusterFunction = stats::kmeans,
  nWords = 4,
  nCluster = NULL,
  show_category_size_legend = TRUE,
  edge_filter = "threshold",
  top_k = 5,
  target_density = 0.1,
  edge_diagnostic = FALSE,
  ...
)

## S4 method for signature 'compareClusterResult'
emapplot(
  x,
  layout = igraph::layout_with_kk,
  coords = NULL,
  showCategory = 30,
  color = "p.adjust",
  size_category = 1,
  min_edge = 0.2,
  color_edge = "grey",
  size_edge = 0.5,
  group = NULL,
  group_legend = TRUE,
  node_label = "category",
  node_label_size = 5,
  pie = "equal",
  layer = NULL,
  label_format = 30,
  clusterFunction = stats::kmeans,
  nWords = 4,
  nCluster = NULL,
  show_category_size_legend = TRUE,
  edge_filter = "threshold",
  top_k = 5,
  target_density = 0.1,
  edge_diagnostic = FALSE,
  ...
)

## S4 method for signature 'mnseaResult'
emapplot(
  x,
  layout = igraph::layout_with_kk,
  coords = NULL,
  showCategory = 30,
  color = "p.adjust",
  size_category = 1,
  min_edge = 0.2,
  color_edge = "grey",
  size_edge = 0.5,
  group = NULL,
  group_legend = TRUE,
  node_label = "category",
  node_label_size = 5,
  pie = "equal",
  layer = NULL,
  label_format = 30,
  clusterFunction = stats::kmeans,
  nWords = 4,
  nCluster = NULL,
  show_category_size_legend = TRUE,
  edge_filter = "threshold",
  top_k = 5,
  target_density = 0.1,
  edge_diagnostic = FALSE,
  ...
)

prepare_emapplot_data(
  x,
  showCategory,
  color,
  min_edge,
  size_edge,
  edge_filter = "threshold",
  top_k = 5,
  target_density = 0.1
)

Arguments

x

Enrichment result.
layout

igraph layout
coords

optional user-supplied coordinate data.frame with x, y and row names matching node labels.
showCategory

number of categories to display or a vector of terms.
color

Variable used to color enriched terms, e.g. 'pvalue', 'p.adjust' or 'qvalue'.
size_category

relative size of the categories
min_edge

The minimum similarity threshold for whether two nodes are connected, should be between 0 and 1, default value is 0.2.
color_edge

color of the network edge
size_edge

relative size of edge width
group

Deprecated. group = TRUE is node_label = "category_grouped"; group = FALSE is node_label = "none". Use node_label instead.
group_legend

logical, if TRUE, draw a legend for the groups.
node_label

Select which labels to display and whether the groups are outlined. One of:

node_label group outline group labels category labels
"none" no no no
"category" no no yes
"category_grouped" yes no yes
"group" yes yes no
"all" yes yes yes
node_label_size

size of node label, default is 5.
pie

one of 'equal' or 'Count' to set the slice ratio of the pies
layer

optional layer or layers to retain for mnseaResult plots.
label_format

a numeric value sets wrap length, alternatively a custom function to format axis labels.
clusterFunction

clustering method function, such as stats::kmeans (default), cluster::clara, cluster::fanny, or cluster::pam.
nWords

Numeric, the number of words in the cluster tags, the default value is 4.
nCluster

Numeric, the number of clusters, the default value is square root of the number of nodes.
show_category_size_legend

Logical, whether to draw the category-size annotation legend for compareCluster pie charts.
edge_filter

Edge filtering strategy. min_edge applies to all three; top_k only applies to "top_k" and target_density only to "adaptive". Supplying a non-default value for the inactive one warns rather than being ignored silently.
top_k

Number of strongest neighbors per term. Only used when edge_filter = "top_k".
target_density

Target proportion of unique term pairs. Only used when edge_filter = "adaptive".
edge_diagnostic

Logical; if TRUE, attach a one-row enrichplot_edge_diagnostic data frame to the returned plot. The diagnostic reports retained pairs, rendered edge rows and graph density.
...

Additional parameters

Details

This function visualizes gene sets as a network (i.e. enrichment map). Mutually overlapping gene sets tend to cluster together, making it easier for interpretation. When the similarity between terms meets a certain threshold (default is 0.2, adjusted by parameter min_edge), there will be edges between terms. The stronger the similarity, the shorter and thicker the edges. The similarity between terms is obtained by the function pairwise_termsim. Details of the similarity calculation can be found in its documentation: pairwise_termsim().

Value

ggplot object

Author(s)

Guangchuang Yu

Examples

## Not run: 
    library(DOSE)
    data(geneList)
    de <- names(geneList)[1:100]
    x <- enrichDO(de)
    x2 <- pairwise_termsim(x)
    emapplot(x2)
    # use `layout` to change the layout of map
    emapplot(x2, layout = "star")
    # use `showCategory` to  select the displayed terms. It can be a number of a vector of terms.
    emapplot(x2, showCategory = 10)
    categories <- c("pre-malignant neoplasm", "intestinal disease",
                   "breast ductal carcinoma")
    emapplot(x2, showCategory = categories)

    # It can also graph compareClusterResult
    library(clusterProfiler)
    library(DOSE)
    library(org.Hs.eg.db)
    data(gcSample)
    xx <- compareCluster(gcSample, fun="enrichGO", OrgDb="org.Hs.eg.db")
    xx2 <- pairwise_termsim(xx)
    emapplot(xx2)

## End(Not run)

Diagnose enrichment-map edge density

Description

Diagnose enrichment-map edge density

Usage

emapplot_edge_density(
  pair_sim,
  min_edge = 0.2,
  edge_filter = "threshold",
  top_k = 5,
  target_density = 0.1,
  dense_threshold = 0.2
)

Arguments

pair_sim

A square numeric similarity matrix.
min_edge

minimum similarity threshold for "threshold" mode.
edge_filter

edge filtering strategy.
top_k

number of neighbors per term for "top_k" mode.
target_density

target proportion of unique pairs for "adaptive".
dense_threshold

density above which a higher threshold is suggested.

Value

A one-row data.frame with retained pair/edge counts, density, filter settings, and a suggestion when the graph is dense.

Predefined color palettes

Description

Predefined color palettes

Usage

enrichplot_point_shape

Plot-facing helpers for mnseaResult

Description

These helpers convert explanation-ready outputs from enrichit into stable data structures that are easier to consume in enrichplot.

Usage

fortify_mnsea_contribution(
  x,
  level = c("pathway", "feature"),
  pathway_id = NULL,
  layer = NULL,
  ...
)

fortify_mnsea_subnetwork(
  x,
  pathway_id = NULL,
  layer = NULL,
  include_couplings = TRUE,
  include_isolated = FALSE,
  ...
)

Arguments

x

A mnseaResult object.
level

One of "pathway" or "feature".
pathway_id

Optional pathway ID.
layer

Optional layer or layers to retain.
...

Additional parameters reserved for future extensions.
include_couplings

Logical, whether to keep inter-layer coupling edges.
include_isolated

Logical, whether to keep nodes without retained edges.

Value

fortify_mnsea_contribution() returns a data.frame. fortify_mnsea_subnetwork() returns a list containing standardized nodes and edges tables together with the selected pathway metadata.

fortify

Description

convert compareClusterResult to a data.frame that ready for plot

convert enrichResult object for ggplot2

Usage

## S3 method for class 'compareClusterResult'
fortify(
  model,
  data,
  showCategory = 5,
  by = "geneRatio",
  split = NULL,
  includeAll = TRUE,
  ...
)

## S3 method for class 'enrichResult'
fortify(
  model,
  data,
  showCategory = 5,
  by = "Count",
  order = FALSE,
  drop = FALSE,
  split = NULL,
  ...
)

## S3 method for class 'nseaResult'
fortify(
  model,
  data,
  showCategory = 5,
  by = "Count",
  order = FALSE,
  drop = FALSE,
  split = NULL,
  ...
)

## S3 method for class 'mnseaResult'
fortify(
  model,
  data,
  showCategory = 5,
  by = c("p.adjust", "NES", "contribution", "share"),
  order = FALSE,
  drop = FALSE,
  split = NULL,
  level = c("result", "pathway"),
  layer = NULL,
  ...
)

Arguments

model

'enrichResult' or 'compareClusterResult' object
data

not use here
showCategory

Category numbers to show
by

one of Count and GeneRatio
split

separate result by 'split' variable
includeAll

logical
...

additional parameter
order

logical
drop

logical
level

One of "result" or "pathway" for mnseaResult.
layer

Optional layer or layers to retain for pathway-level output.

Value

data.frame

data.frame

Author(s)

Guangchuang Yu

geom_gsea_gene

Description

label genes in running score plot

Usage

geom_gsea_gene(
  genes,
  mapping = NULL,
  geom = ggplot2::geom_text,
  ...,
  geneSet = NULL
)

Arguments

genes

selected genes to be labeled
mapping

aesthetic mapping, default is NULL
geom

geometric layer to plot the gene labels, default is geom_text
...

additional parameters passed to the 'geom'
geneSet

choose which gene set(s) to be label if the plot contains multiple gene sets

Value

ggplot object

Author(s)

Guangchuang Yu

Color utility functions for enrichplot package

Description

This file contains all color-related helper functions Get default enrichplot colors

Usage

get_enrichplot_color(n = 2)

Arguments

n

number of colors (2 or 3)

Value

color vector

ggtable

Description

plot table

Usage

ggtable(d, p = NULL)

Arguments

d

data frame
p

ggplot object to extract color to color rownames(d), optional

Value

ggplot object

Author(s)

guangchuang yu

goplot

Description

Plot induced GO DAG of significant terms

Usage

goplot(
  x,
  showCategory = 10,
  color = "p.adjust",
  layout = "sugiyama",
  geom = "text",
  ...
)

## S4 method for signature 'enrichResult'
goplot(
  x,
  showCategory = 10,
  color = "p.adjust",
  layout = igraph::layout_with_sugiyama,
  geom = "text",
  ...
)

## S4 method for signature 'gseaResult'
goplot(
  x,
  showCategory = 10,
  color = "p.adjust",
  layout = igraph::layout_with_sugiyama,
  geom = "text",
  ...
)

Arguments

x

enrichment result.
showCategory

number of categories to display, or a vector of terms.
color

variable used to color enriched terms, e.g. 'pvalue', 'p.adjust', or 'qvalue'.
layout

layout of the map
geom

label geom, one of 'label' or 'text'
...

additional parameters.

Value

ggplot object

Author(s)

Guangchuang Yu

Examples

## Not run: 
	library(clusterProfiler)
  data(geneList, package = "DOSE")
	de <- names(geneList)[1:100]
	yy <- enrichGO(de, 'org.Hs.eg.db', ont="BP", pvalueCutoff=0.01)
    goplot(yy)
    goplot(yy, showCategory = 5)

## End(Not run)

gseadist

Description

plot logFC distribution of selected gene sets

Usage

gseadist(x, IDs, type = "density")

Arguments

x

GSEA result
IDs

gene set IDs
type

one of 'density' or 'boxplot'

Value

distribution plot

Author(s)

Guangchuang Yu

gseaplot

Description

Visualize GSEA analysis results

Usage

gseaplot(x, geneSetID, by = "all", title = "", ...)

## S4 method for signature 'gseaResult'
gseaplot(
  x,
  geneSetID,
  by = "all",
  title = "",
  color = "black",
  color.line = "green",
  color.vline = "#FA5860",
  ...
)

## S4 method for signature 'mnseaResult'
gseaplot(
  x,
  geneSetID,
  by = "all",
  title = "",
  color = "black",
  color.line = "green",
  color.vline = "#FA5860",
  layer = NULL,
  ...
)

gseaplot.gseaResult(
  x,
  geneSetID,
  by = "all",
  title = "",
  color = "black",
  color.line = "green",
  color.vline = "#FA5860",
  ...
)

Arguments

x

gseaResult object
geneSetID

geneSet ID
by

one of "runningScore" or "position"
title

plot title
...

additional parameters
color

color of line segments
color.line

color of running enrichment score line
color.vline

color of vertical line indicating the maximum/minimal running enrichment score
layer

Optional mnsea layer. When NULL, use collapsed scores.

Details

Plotting function for gseaResult

Value

ggplot2 object

ggplot2 object

Author(s)

Guangchuang Yu

Examples

library(DOSE)
data(geneList)
x <- gseDO(geneList)
gseaplot(x, geneSetID=1)

gseaplot2

Description

GSEA plot that mimic the plot generated by broad institute's GSEA software

Usage

gseaplot2(
  x,
  geneSetID,
  title = "",
  color = "green",
  base_size = 11,
  rel_heights = c(1.5, 0.5, 1),
  subplots = 1:3,
  pvalue_table = FALSE,
  pvalue_table_columns = c("NES", "p.adjust"),
  pvalue_table_rownames = "Description",
  ES_geom = "line",
  layer = NULL
)

Arguments

x

gseaResult object
geneSetID

gene set ID
title

plot title
color

color of running enrichment score line
base_size

base font size
rel_heights

relative heights of subplots
subplots

which subplots to be displayed
pvalue_table

whether add pvalue table
pvalue_table_columns

selected columns to be plotted in the pvalue_table. Defaults to c("NES", "p.adjust"), but can be set to any columns of the enrichment result (e.g. "pvalue", "qvalue").
pvalue_table_rownames

selected column as the rownames of the pvalue_table. If set to NULL, no rownames will be displayed.
ES_geom

geom for plotting running enrichment score, one of 'line' or 'dot'
layer

Optional mnsea layer. When NULL, use collapsed scores.

Value

plot

Author(s)

Guangchuang Yu

gsearank

Description

plot ranked list of genes with running enrichment score as bar height

Usage

gsearank(x, geneSetID, title = "", output = "plot", layer = NULL)

Arguments

x

gseaResult object
geneSetID

gene set ID
title

plot title
output

one of 'plot' or 'table' (for exporting data)
layer

Optional mnsea layer. When NULL, use collapsed scores.

Value

ggplot object

Author(s)

Guangchuang Yu

gsInfo

Description

extract gsea result of selected geneSet

Usage

gsInfo(object, geneSetID, ...)

Arguments

object

gseaResult object
geneSetID

gene set ID
...

additional arguments passed to methods (e.g. layer for mnseaResult)

Value

data.frame

Author(s)

Guangchuang Yu

heatplot

Description

Heatmap-like plot for functional classification

Usage

heatplot(
  x,
  showCategory = 30,
  showTop = NULL,
  symbol = "rect",
  foldChange = NULL,
  pvalue = NULL,
  label_format = 30,
  pathway_id = NULL,
  layer = NULL,
  value = c("score", "abs_score", "share", "contribution"),
  ...
)

## S4 method for signature 'enrichResult'
heatplot(
  x,
  showCategory = 30,
  showTop = NULL,
  symbol = "rect",
  foldChange = NULL,
  pvalue = NULL,
  label_format = 30,
  pathway_id = NULL,
  layer = NULL,
  value = c("score", "abs_score", "share", "contribution"),
  ...
)

## S4 method for signature 'gseaResult'
heatplot(
  x,
  showCategory = 30,
  showTop = NULL,
  symbol = "rect",
  foldChange = NULL,
  pvalue = NULL,
  label_format = 30,
  pathway_id = NULL,
  layer = NULL,
  value = c("score", "abs_score", "share", "contribution"),
  ...
)

## S4 method for signature 'mnseaResult'
heatplot(
  x,
  showCategory = 30,
  showTop = NULL,
  symbol = "rect",
  foldChange = NULL,
  pvalue = NULL,
  label_format = 30,
  pathway_id = NULL,
  layer = NULL,
  value = c("score", "abs_score", "share", "contribution"),
  ...
)

prepare_heatplot_data(x, showCategory, showTop, foldChange, pvalue)

Arguments

x

enrichment result.
showCategory

number of enriched terms to display
showTop

number of top genes ranked by abs(foldChange) * frequency to be shown in the heatmap, default NULL means all genes are shown
symbol

symbol of the nodes, one of "rect" (the default) or "dot"
foldChange

fold change.
pvalue

pvalue of genes
label_format

a numeric value sets wrap length, alternatively a custom function to format axis labels. by default wraps names longer than 30 characters
pathway_id

optional pathway ID for pathway-specific mnseaResult heatmaps.
layer

optional layer or layers to retain for mnseaResult plots.
value

fill value for mnseaResult; use "share" or "contribution" for term-layer heatmaps and "score" or "abs_score" for pathway-specific feature heatmaps.
...

Additional parameters

Value

ggplot object

Author(s)

Guangchuang Yu

Examples

## Not run: 
library(DOSE)
data(geneList)
de <- names(geneList)[1:100]
x <- enrichDO(de)
heatplot(x)

## End(Not run)

hplot

Description

Horizontal plot for GSEA result

Usage

hplot(x, geneSetID, layer = NULL)

Arguments

x

gseaResult object
geneSetID

gene set ID
layer

Optional mnsea layer. When NULL, use collapsed scores.

Value

horizontal plot

Author(s)

Guangchuang Yu

import_enrichr

Description

Import enrichr results

Usage

import_enrichr(
  x,
  db = 1,
  gene = NULL,
  universe = NULL,
  ontology = "UNKNOWN",
  organism = "UNKNOWN",
  keytype = "UNKNOWN",
  pAdjustMethod = "BH",
  ...
)

Arguments

x

result table (data.frame) or the list of tables returned by enrichR::enrichr()
db

if x is a list, the name or index of the database table to import
gene

query gene vector used in the analysis
universe

background gene vector used in the analysis
ontology, organism, keytype

metadata stored in the object slots
pAdjustMethod

method used to compute p.adjust when the table has no adjusted p-value column
...

additional parameters passed to as_enrichResult

Details

Converts the output of enrichR::enrichr() (a list of result tables, one per database, or a single result table) into an enrichResult object compatible with 'enrichplot' visualizations. The gene vector used in the analysis should be supplied for exact GeneRatio values; universe enables BgRatio/RichFactor calculation.

Value

An enrichResult object

import_fgsea

Description

Import fgsea results

Usage

import_fgsea(
  x,
  stats,
  geneSets = NULL,
  setType = "UNKNOWN",
  organism = "UNKNOWN",
  keytype = "UNKNOWN",
  exponent = 1,
  scoreType = "std",
  pAdjustMethod = "BH",
  ...
)

Arguments

x

result table returned by fgsea::fgsea()
stats

named numeric vector of ranked statistics, sorted in descending order (sorted automatically with a warning if not)
geneSets

the pathways list used as fgsea() input
setType, organism, keytype

metadata stored in the object slots
exponent, scoreType

parameters used to recompute missing rank/leading_edge/core_enrichment columns
pAdjustMethod

method used to compute p.adjust when the table has no adjusted p-value column
...

additional parameters passed to as_gseaResult

Details

Converts the result table returned by fgsea::fgsea() into a gseaResult object. The ranked statistics vector (stats) used as fgsea() input is required, as most GSEA visualizations consume the ranked list. Passing the pathways list used in the analysis enables exact running-score plots; without it, gene sets are rebuilt from the leading edge and plots are approximate.

Value

A gseaResult object

import_gprofiler2

Description

Import g:Profiler (gprofiler2) results

Usage

import_gprofiler2(
  x,
  gene = NULL,
  ontology = "UNKNOWN",
  organism = "UNKNOWN",
  keytype = "UNKNOWN",
  pAdjustMethod = "BH",
  ...
)

Arguments

x

the object returned by gprofiler2::gost(), or its result component
gene

query gene vector; used only when the table has no query_size column
ontology, organism, keytype

metadata stored in the object slots
pAdjustMethod

method used to compute p.adjust when the table has no adjusted p-value column
...

additional parameters passed to as_enrichResult

Details

Converts the result of gprofiler2::gost() (the list it returns, or its result data.frame) into an enrichResult object. Only the over-representation mode is mapped in this version.

Value

An enrichResult object

import_webgestalt

Description

Import WebGestaltR results

Usage

import_webgestalt(
  x,
  gene = NULL,
  universe = NULL,
  ontology = "UNKNOWN",
  organism = "UNKNOWN",
  keytype = "UNKNOWN",
  pAdjustMethod = "BH",
  ...
)

Arguments

x

result table (data.frame) returned by WebGestaltR()
gene

query gene vector used in the analysis
universe

background gene vector used in the analysis
ontology, organism, keytype

metadata stored in the object slots
pAdjustMethod

method used to compute p.adjust when the table has no adjusted p-value column
...

additional parameters passed to as_enrichResult

Details

Converts the result table returned by WebGestaltR() into an enrichResult object. The gene vector used in the analysis should be supplied for exact GeneRatio values.

Value

An enrichResult object

manhattanplot

Description

Manhattan plot for enrichment result

Usage

manhattanplot(x, ...)

## S4 method for signature 'enrichResult'
manhattanplot(
  x,
  color = "p.adjust",
  showCategory = 5,
  size = "Count",
  split = NULL,
  font.size = 12,
  title = "",
  label_format = 30,
  ...
)

## S4 method for signature 'gseaResult'
manhattanplot(
  x,
  color = "p.adjust",
  showCategory = 5,
  size = "Count",
  split = NULL,
  font.size = 12,
  title = "",
  label_format = 30,
  ...
)

## S4 method for signature 'compareClusterResult'
manhattanplot(
  x,
  color = "p.adjust",
  showCategory = 5,
  split = NULL,
  font.size = 12,
  title = "",
  size = "Count",
  includeAll = TRUE,
  label_format = 30,
  ...
)

## S4 method for signature 'enrichResultList'
manhattanplot(
  x,
  color = "p.adjust",
  showCategory = 5,
  size = "Count",
  split = NULL,
  font.size = 12,
  title = "",
  label_format = 30,
  ...
)

## S4 method for signature 'gseaResultList'
manhattanplot(
  x,
  color = "p.adjust",
  showCategory = 5,
  size = "Count",
  split = NULL,
  font.size = 12,
  title = "",
  label_format = 30,
  ...
)

## S4 method for signature 'list'
manhattanplot(
  x,
  color = "p.adjust",
  showCategory = 5,
  size = "Count",
  split = NULL,
  font.size = 12,
  title = "",
  label_format = 30,
  ...
)

Arguments

x

enrichment result.
...

additional parameters.
color

variable used to color enriched terms, e.g. 'pvalue', 'p.adjust', or 'qvalue'.
showCategory

number of categories to display, or a vector of terms.
size

variable used to scale category size, one of "geneRatio", "Percentage", or "count".
split

apply showCategory to each category specified by split, e.g., "ONTOLOGY", "category", or "intersect". Default is NULL.
font.size

font size.
title

figure title.
label_format

a numeric wrap width, or a custom function to format axis labels.
includeAll

logical value.

Value

ggplot object

Author(s)

Guangchuang Yu

mechanismflow

Description

Pathway state transition flow

Usage

mechanismflow(
  x,
  reference = NULL,
  flow_var = c("NES", "delta_NES", "leading_edge_size"),
  thresholds = list(rewire = 0.5, nes = 0.2),
  ...
)

## S4 method for signature 'mnseaResult'
mechanismflow(
  x,
  reference = NULL,
  flow_var = c("NES", "delta_NES", "leading_edge_size"),
  thresholds = list(rewire = 0.5, nes = 0.2),
  ...
)

## S4 method for signature 'nseaResult'
mechanismflow(
  x,
  reference = NULL,
  flow_var = c("NES", "delta_NES", "leading_edge_size"),
  thresholds = list(rewire = 0.5, nes = 0.2),
  ...
)

## S4 method for signature 'list'
mechanismflow(
  x,
  reference = NULL,
  flow_var = c("NES", "delta_NES", "leading_edge_size"),
  thresholds = list(rewire = 0.5, nes = 0.2),
  ...
)

Arguments

x

A mnseaResult, nseaResult, or a named list of results.
reference

An optional reference result used to compute delta_NES. For list input, the first element is used as the default reference.
flow_var

One of "NES", "delta_NES", or "leading_edge_size"; controls flow magnitude (line width and point size).
thresholds

Optional named list with rewire and nes numeric thresholds used to classify mechanism states.
...

Additional parameters passed to plot methods.

Details

mechanismflow() draws pathway state transitions across contexts (conditions or layers). Line width and point size encode flow magnitude, while point shape encodes the mechanism state.

Value

A ggplot object.

pairwise_termsim

Description

Get the similarity matrix

Usage

pairwise_termsim(
  x,
  method = "JC",
  semData = NULL,
  showCategory = NULL,
  layer = NULL
)

## S4 method for signature 'enrichResult'
pairwise_termsim(
  x,
  method = "JC",
  semData = NULL,
  showCategory = NULL,
  layer = NULL
)

## S4 method for signature 'gseaResult'
pairwise_termsim(
  x,
  method = "JC",
  semData = NULL,
  showCategory = NULL,
  layer = NULL
)

## S4 method for signature 'mnseaResult'
pairwise_termsim(
  x,
  method = "JC",
  semData = NULL,
  showCategory = NULL,
  layer = NULL
)

## S4 method for signature 'compareClusterResult'
pairwise_termsim(
  x,
  method = "JC",
  semData = NULL,
  showCategory = NULL,
  layer = NULL
)

prepare_pairwise_termsim_data(x, showCategory)

pairwise_termsim.enrichResult(
  x,
  method = "JC",
  semData = NULL,
  showCategory = NULL
)

pairwise_termsim.compareClusterResult(
  x,
  method = "JC",
  semData = NULL,
  showCategory = NULL
)

Arguments

x

enrichment result.
method

method of calculating the similarity between nodes, one of "Resnik", "Lin", "Rel", "Jiang", "Wang", and "JC" (Jaccard similarity coefficient) methods.
semData

GOSemSimDATA object, can be obtained through GOSemSim::godata.
showCategory

number of enriched terms to be calculated. The default value is the number of enriched terms, or 200 if the number of enriched terms exceeds 200.
layer

optional layer or layers to retain when computing feature overlap for mnseaResult; when NULL, features from all layers are used.

Details

This function adds a similarity matrix to the termsim slot of the enrichment result. Users can use the method parameter to select the method of calculating the similarity. The Jaccard correlation coefficient (JC) is used by default, and it applies to all situations. When users want to calculate the correlation between GO terms or DO terms, they can also choose "Resnik", "Lin", "Rel" or "Jiang" (they are semantic similarity calculation methods from the 'GOSemSim' package), and at this time, the user needs to provide the semData parameter, which can be obtained through GOSemSim::godata().

Examples

## Not run: 
    library(clusterProfiler)
    library(org.Hs.eg.db)
    library(enrichplot)
    library(GOSemSim)
    library(DOSE)
    data(geneList)
    gene <- names(geneList)[abs(geneList) > 2]
    ego <- enrichGO(gene  = gene,
        universe      = names(geneList),
        OrgDb         = org.Hs.eg.db,
        ont           = "BP",
        pAdjustMethod = "BH",
        pvalueCutoff  = 0.01,
        qvalueCutoff  = 0.05,
        readable      = TRUE)
    d <- godata('org.Hs.eg.db', ont="BP")
    ego2 <- pairwise_termsim(ego, method="Wang", semData = d)
    emapplot(ego2)
    emapplot_cluster(ego2)
   
## End(Not run)

phaseplot

Description

Phase plot for enrichment-shift versus rewiring

Usage

phaseplot(
  x,
  reference = NULL,
  selected_layer = NULL,
  reference_layer = NULL,
  x_axis = NULL,
  size_var = c("leading_edge_size", "leading_edge_overlap"),
  showCategory = 30,
  thresholds = list(rewire = 0.5, nes = 0.2),
  ...
)

## S4 method for signature 'nseaResult'
phaseplot(
  x,
  reference = NULL,
  selected_layer = NULL,
  reference_layer = NULL,
  x_axis = NULL,
  size_var = c("leading_edge_size", "leading_edge_overlap"),
  showCategory = 30,
  thresholds = list(rewire = 0.5, nes = 0.2),
  ...
)

## S4 method for signature 'mnseaResult'
phaseplot(
  x,
  reference = NULL,
  selected_layer = NULL,
  reference_layer = NULL,
  x_axis = NULL,
  size_var = c("leading_edge_size", "leading_edge_overlap"),
  showCategory = 30,
  thresholds = list(rewire = 0.5, nes = 0.2),
  ...
)

Arguments

x

A nseaResult or mnseaResult object.
reference

An optional reference result (another nseaResult, mnseaResult, or a results data.frame). When supplied, phaseplot() computes delta_NES = NES - reference_NES and uses it as the default x-axis.
selected_layer

Optional layer name for mnseaResult.
reference_layer

Optional reference layer name for mnseaResult.
x_axis

One of "delta_NES" or "NES". By default, delta_NES is used when reference is supplied, otherwise NES.
size_var

One of "leading_edge_size" or "leading_edge_overlap".
showCategory

Number (or vector) of pathways to display.
thresholds

Optional named list with rewire and nes numeric thresholds used to classify mechanism states.
...

Additional parameters passed to plot methods.

Details

phaseplot() places each pathway on a two-dimensional plane: enrichment strength (or shift) on the x-axis and rewiring score on the y-axis. Point size and color encode leading-edge information and mechanism class.

Value

A ggplot object.

Internal plot function for plotting compareClusterResult

Description

Internal plot function for plotting compareClusterResult

Usage

plotting.clusterProfile(
  clProf.reshape.df,
  x = ~Cluster,
  type = "dot",
  colorBy = "p.adjust",
  by = "geneRatio",
  title = "",
  font.size = 12,
  width = NULL
)

Arguments

clProf.reshape.df

data frame of compareCluster result
x

x variable
type

one of dot and bar
colorBy

one of pvalue or p.adjust
by

one of percentage and count
title

graph title
font.size

graph font size
width

width of the plot

Value

ggplot object

Author(s)

Guangchuang Yu https://yulab-smu.top

pmcplot

Description

PubMed Central Trend plot

Usage

pmcplot(query, period, proportion = TRUE, data = NULL)

Arguments

query

query terms
period

period of query in the unit of year
proportion

If TRUE, use query_hits/all_hits, otherwise use query_hits.
data

Optional data.frame with query, year, query_hits, and all_hits columns. When supplied, no Europe PMC request is made.

Value

ggplot object

Author(s)

Guangchuang Yu

Category-Gene-Network Plot

Description

Category-gene-network plot

Usage

prepare_cnetplot_data(x, showCategory, foldChange)

## S3 method for class 'enrichResult'
cnetplot(
  x,
  layout = igraph::layout_with_kk,
  showCategory = 5,
  color_category = "#E5C494",
  size_category = 1,
  color_item = "#B3B3B3",
  size_item = 1,
  color_edge = "grey",
  size_edge = 0.5,
  categorySizeBy = ~itemNum,
  node_label = "all",
  node_label_size = NULL,
  foldChange = NULL,
  fc_threshold = NULL,
  hilight = "none",
  hilight_alpha = 0.3,
  ...
)

## S3 method for class 'gseaResult'
cnetplot(
  x,
  layout = igraph::layout_with_kk,
  showCategory = 5,
  color_category = "#E5C494",
  size_category = 1,
  color_item = "#B3B3B3",
  size_item = 1,
  color_edge = "grey",
  size_edge = 0.5,
  categorySizeBy = ~itemNum,
  node_label = "all",
  node_label_size = NULL,
  foldChange = NULL,
  fc_threshold = NULL,
  hilight = "none",
  hilight_alpha = 0.3,
  ...
)

prepare_mnsea_cnetplot_data(
  x,
  pathway_id,
  layer,
  include_couplings,
  include_isolated = FALSE
)

## S3 method for class 'mnseaResult'
cnetplot(
  x,
  layout = igraph::layout_with_kk,
  showCategory = 5,
  color_category = "#E5C494",
  size_category = 1,
  color_item = "#B3B3B3",
  size_item = 1,
  color_edge = "grey",
  size_edge = 0.5,
  categorySizeBy = ~itemNum,
  node_label = "all",
  node_label_size = NULL,
  foldChange = NULL,
  fc_threshold = NULL,
  hilight = "none",
  hilight_alpha = 0.3,
  pathway_id = NULL,
  layer = NULL,
  include_couplings = TRUE,
  ...
)

## S3 method for class 'compareClusterResult'
cnetplot(
  x,
  layout = igraph::layout_with_kk,
  showCategory = 5,
  color_category = "#E5C494",
  size_category = 1,
  color_item = "#B3B3B3",
  size_item = 1,
  color_edge = "grey",
  size_edge = 0.5,
  categorySizeBy = ~itemNum,
  node_label = "all",
  node_label_size = NULL,
  foldChange = NULL,
  fc_threshold = NULL,
  hilight = "none",
  hilight_alpha = 0.3,
  ...
)

Arguments

x

input object
showCategory

number of categories to display or a vector of terms.
foldChange

numeric values to color the item (e.g., fold change of gene expression values)
layout

network layout
color_category

color of category nodes
size_category

relative size of the category nodes
color_item

color of item nodes
size_item

relative size of the item nodes (e.g., genes)
color_edge

color of edge
size_edge

relative size of edge
categorySizeBy

An expression (e.g., itemNum, p.adjust) or a formula (e.g., ~ -log10(p.adjust)) to set the category node size. For compareClusterResult, this controls the category pie size.
node_label

one of 'all', 'none', 'category', 'item', 'exclusive' or 'share'. 'exclusive' labels genes that uniquely belong to categories; 'share' labels genes that are shared between categories.
node_label_size

size of the node label text. When NULL (default) the built-in label size is used.
fc_threshold

threshold for filtering genes by absolute fold change (e.g., fc_threshold = 1 keeps only genes with |foldChange| > 1).
hilight

selected categories to be highlighted
hilight_alpha

transparency value for non-highlighted items
...

additional parameters. Legacy categorySize, circular, colorEdge, pie, split, and includeAll arguments are also accepted.
pathway_id

optional pathway ID for mnseaResult subnetworks.
layer

optional layer or layers to retain for mnseaResult plots.
include_couplings

logical, whether inter-layer coupling edges should be kept in mnseaResult network plots.
include_isolated

logical, whether isolated feature nodes should be kept in mnseaResult subnetworks.

Details

For mnseaResult, cnetplot() visualizes a pathway-specific multilayer subnetwork returned by fortify_mnsea_subnetwork(). Feature nodes are grouped by layer, a synthetic pathway node is added as the anchor, and optional coupling edges connect features across layers.

See Also

cnetplot

rewireplot

Description

Pathway-specific rewiring plot

Usage

rewireplot(x, ...)

## S4 method for signature 'nseaResult'
rewireplot(x, ...)

## S4 method for signature 'mnseaResult'
rewireplot(x, ...)

Arguments

x

A mnseaResult object.
...

Additional parameters passed to plot methods.

Value

A ggplot object.

ridgeplot

Description

Ridgeline plot for GSEA result

Usage

ridgeplot(
  x,
  showCategory = 30,
  fill = "p.adjust",
  core_enrichment = TRUE,
  label_format = 30,
  ...
)

## S4 method for signature 'gseaResult'
ridgeplot(
  x,
  showCategory = 30,
  fill = "p.adjust",
  core_enrichment = TRUE,
  label_format = 30,
  ...
)

## S4 method for signature 'mnseaResult'
ridgeplot(
  x,
  showCategory = 30,
  fill = "p.adjust",
  core_enrichment = TRUE,
  label_format = 30,
  orderBy = "NES",
  decreasing = FALSE,
  stat = "density_ridges",
  layer = NULL,
  ...
)

ridgeplot.gseaResult(
  x,
  showCategory = 30,
  fill = "p.adjust",
  core_enrichment = TRUE,
  label_format = 30,
  orderBy = "NES",
  decreasing = FALSE,
  stat = "density_ridges"
)

Arguments

x

gseaResult object
showCategory

number of categories to display or a vector of terms.
fill

one of "pvalue", "p.adjust", "qvalue"
core_enrichment

whether to use only core_enriched genes
label_format

a numeric value setting the wrap length, alternatively a custom function to format axis labels.
...

additional parameters.
orderBy

The order of the Y-axis
decreasing

logical. Should the orderBy order be increasing or decreasing?
stat

statistic passed to ggridges::geom_density_ridges().
layer

Optional mnsea layer. When NULL, use collapsed scores.

Value

ggplot object

Author(s)

Guangchuang Yu

Examples

library(DOSE)
data(geneList)
x <- gseDO(geneList)
ridgeplot(x)

Helper function to set color scale for enrichplot

Description

Helper function to set color scale for enrichplot

Usage

set_enrichplot_color(
  colors = get_enrichplot_color(2),
  type = "color",
  name = NULL,
  .fun = NULL,
  reverse = TRUE,
  transform = "identity",
  ...
)

Arguments

colors

user provided color vector
type

one of 'color', 'colour' or 'fill'
name

name of the color legend
.fun

force to use user provided color scale function
reverse

whether reverse the color scheme
transform

transform the color scale
...

additional parameters

Value

a color scale

Author(s)

Guangchuang Yu

ssplot

Description

Similarity Space Plot for enrichment analysis

Usage

ssplot(x, ...)

## S4 method for signature 'enrichResult'
ssplot(
  x,
  showCategory = 30,
  ...,
  min_edge = 0.2,
  size_edge = 0.5,
  edge_filter = "threshold",
  top_k = 5,
  target_density = 0.1,
  edge_diagnostic = FALSE
)

## S4 method for signature 'gseaResult'
ssplot(
  x,
  showCategory = 30,
  ...,
  min_edge = 0.2,
  size_edge = 0.5,
  edge_filter = "threshold",
  top_k = 5,
  target_density = 0.1,
  edge_diagnostic = FALSE
)

## S4 method for signature 'mnseaResult'
ssplot(
  x,
  showCategory = 30,
  ...,
  min_edge = 0.2,
  size_edge = 0.5,
  edge_filter = "threshold",
  top_k = 5,
  target_density = 0.1,
  edge_diagnostic = FALSE
)

## S4 method for signature 'compareClusterResult'
ssplot(
  x,
  showCategory = 30,
  ...,
  min_edge = 0.2,
  size_edge = 0.5,
  edge_filter = "threshold",
  top_k = 5,
  target_density = 0.1,
  edge_diagnostic = FALSE
)

ssplot.enrichResult(
  x,
  showCategory = 30,
  drfun = NULL,
  dr.params = list(),
  node_label = "group",
  min_edge = 0.2,
  size_edge = 0.5,
  edge_filter = "threshold",
  top_k = 5,
  target_density = 0.1,
  edge_diagnostic = FALSE,
  ...
)

ssplot.compareClusterResult(
  x,
  showCategory = 30,
  pie = "equal",
  drfun = NULL,
  dr.params = list(),
  node_label = "group",
  min_edge = 0.2,
  size_edge = 0.5,
  edge_filter = "threshold",
  top_k = 5,
  target_density = 0.1,
  edge_diagnostic = FALSE,
  ...
)

Arguments

x

Enrichment result.
...

additional parameters

Additional plotting parameters are inherited from emapplot().
showCategory

number of categories to display or a vector of terms.
min_edge

The minimum similarity threshold for connecting two nodes; should be between 0 and 1. Passed to emapplot().
size_edge

Relative size of the edge width. Passed to emapplot().
edge_filter

Edge filtering strategy: "threshold" (default), "top_k", or "adaptive". Edge filtering affects display only and does not change the dimension-reduction input.
top_k

Number of strongest neighbors retained per term when edge_filter = "top_k".
target_density

Target proportion of unique term pairs when edge_filter = "adaptive".
edge_diagnostic

Logical; if TRUE, attach a one-row edge-density diagnostic data frame to the returned plot.
drfun

Function used for dimension reduction. The default, stats::cmdscale, performs classical multidimensional scaling (MDS); this is one available reduction method. Supply another function here when needed.
dr.params

A named list of arguments passed separately to drfun via tidydr::dr.
node_label

Select which labels to display and whether the groups are outlined. One of:

node_label group outline group labels category labels
"none" no no no
"category" no no yes
"category_grouped" yes no yes
"group" yes yes no
"all" yes yes yes
pie

one of 'equal' or 'Count' to set the slice ratio of the pies

Details

Creates 2D visualization of enrichment results using dimension reduction techniques to show relationships between terms based on similarity.

Value

ggplot object

Author(s)

Guangchuang Yu

Examples

## Not run: 
    library(clusterProfiler)
    library(org.Hs.eg.db)
    library(enrichplot)
    library(GOSemSim)
    library(DOSE)
    data(geneList)
    gene <- names(geneList)[abs(geneList) > 2]
    ego <- enrichGO(gene  = gene,
        universe      = names(geneList),
        OrgDb         = org.Hs.eg.db,
        ont           = "BP",
        pAdjustMethod = "BH",
        pvalueCutoff  = 0.01,
        qvalueCutoff  = 0.05,
        readable      = TRUE)
    d <- godata('org.Hs.eg.db', ont="BP")
    ego2 <- pairwise_termsim(ego, method = "Wang", semData = d)
    ssplot(ego2)
    # Edge controls are forwarded to emapplot; increase min_edge for a
    # less dense display when showing many terms.
    ssplot(ego2, showCategory = 50, min_edge = 0.4, size_edge = 0.5)

## End(Not run)

treeplot

Description

Functional grouping tree diagram for enrichment result of over-representation test or gene set enrichment analysis.

Creates hierarchical tree visualization of enriched terms based on similarity

Usage

treeplot(x, ...)

## S4 method for signature 'enrichResult'
treeplot(x, ...)

## S4 method for signature 'gseaResult'
treeplot(x, ...)

## S4 method for signature 'compareClusterResult'
treeplot(x, ...)

treeplot_internal(
  x,
  showCategory = 30,
  color = "p.adjust",
  size_var = c("Count", "setSize"),
  split = NULL,
  nCluster = 5,
  cluster_method = "ward.D",
  label_format = 30,
  fontsize_tiplab = 4,
  fontsize_cladelab = 4,
  group_color = NULL,
  extend = 0.3,
  hilight = TRUE,
  align = "both",
  hexpand = 0.1,
  tiplab_offset = 0.2,
  cladelab_offset = 1
)

Arguments

x

enrichment result.
...

additional parameters
showCategory

number of enriched terms to display
color

variable to color nodes, e.g. 'p.adjust', 'pvalue', or 'qvalue'
size_var

variable for node size, e.g. 'Count' (for enrichResult) or 'setSize' (for gseaResult)
split

optional result column used to keep split metadata available for faceting
nCluster

number of clusters for tree cutting
cluster_method

hierarchical clustering method
label_format

wrap length for labels or custom formatting function
fontsize_tiplab

font size for tip labels
fontsize_cladelab

font size for clade labels
group_color

vector of colors for groups
extend

extend length for clade labels
hilight

whether to highlight clades
align

alignment for highlight rectangles
hexpand

expand x limits by amount of xrange * hexpand
tiplab_offset

offset for tip labels
cladelab_offset

offset for clade labels

Details

This function visualizes gene sets as a tree. Gene sets with high similarity tend to cluster together, making it easier for interpretation.

Value

ggplot object

ggplot2 object representing the tree plot

Examples

## Not run: 
    library(clusterProfiler)
    library(org.Hs.eg.db)
    library(enrichplot)
    library(GOSemSim)
    library(ggplot2)
    library(DOSE)
    data(geneList)
    gene <- names(geneList)[abs(geneList) > 2]
    ego <- enrichGO(gene  = gene,
        universe      = names(geneList),
        OrgDb         = org.Hs.eg.db,
        ont           = "BP",
        pAdjustMethod = "BH",
        pvalueCutoff  = 0.01,
        qvalueCutoff  = 0.05,
        readable      = TRUE)
    d <- godata('org.Hs.eg.db', ont="BP")
    ego2 <- pairwise_termsim(ego, method = "Wang", semData = d)
    treeplot(ego2, showCategory = 30)
    # use `hilight = FALSE` to remove ggtree::geom_hilight() layer.
    treeplot(ego2, showCategory = 30, hilight = FALSE)
    # use `offset` parameter to adjust the distance of bar and tree.
    treeplot(ego2, showCategory = 30, hilight = FALSE, offset = rel(1.5))
    # use `offset_tiplab` parameter to adjust the distance of nodes and branches.
    treeplot(ego2, showCategory = 30, hilight = FALSE, offset_tiplab = rel(1.5))
    keep <- rownames(ego2@termsim)[c(1:10, 16:20)]
    keep
    treeplot(ego2, showCategory = keep)
    treeplot(ego2, showCategory = 20,
        group_color = c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442"))
    # It can also graph compareClusterResult
    data(gcSample)
    xx <- compareCluster(gcSample, fun="enrichKEGG",
                         organism="hsa", pvalueCutoff=0.05)
    xx <- pairwise_termsim(xx)
    treeplot(xx)

    # use `geneClusterPanel` to change the gene cluster panel.
    treeplot(xx, geneClusterPanel = "dotplot")

    treeplot(xx, geneClusterPanel = "pie")
   
## End(Not run)

upsetplot method

Description

upsetplot method generics

Upsetplot

Usage

upsetplot(
  x,
  n = 10,
  type = "boxplot",
  layer = NULL,
  value = c("score", "abs_score"),
  core_enrichment = FALSE,
  ...
)

## S4 method for signature 'enrichResult'
upsetplot(
  x,
  n = 10,
  type = "boxplot",
  layer = NULL,
  value = c("score", "abs_score"),
  core_enrichment = FALSE,
  ...
)

## S4 method for signature 'gseaResult'
upsetplot(
  x,
  n = 10,
  type = "boxplot",
  layer = NULL,
  value = c("score", "abs_score"),
  core_enrichment = FALSE,
  ...
)

## S4 method for signature 'mnseaResult'
upsetplot(
  x,
  n = 10,
  type = "boxplot",
  layer = NULL,
  value = c("score", "abs_score"),
  core_enrichment = FALSE,
  ...
)

Arguments

x

object
n

number of categories to be plotted
type

one of 'boxplot' or 'violin' for gseaResult / mnseaResult
layer

Optional mnsea layer. When NULL, use collapsed scores.
value

score summary to display for overlapping features.
core_enrichment

logical. Should only core mnsea features be used?
...

additional parameters

Value

plot

Author(s)

Guangchuang Yu

Examples

## Not run: 
library(DOSE)
data(geneList)
de <- names(geneList)[1:100]
x <- enrichDO(de)
upsetplot(x, 8)

## End(Not run)

volplot

Description

Volcano plot for enrichment result

Usage

volplot(
  x,
  color = "zScore",
  xintercept = 1,
  yintercept = 2,
  showCategory = 5,
  label_format = 30,
  ...
)

## S4 method for signature 'enrichResult'
volplot(
  x,
  color = "zScore",
  xintercept = 1,
  yintercept = 2,
  showCategory = 5,
  label_format = 30,
  ...
)

volplot.enrichResult(
  x,
  color = "zScore",
  xintercept = 1,
  yintercept = 2,
  showCategory = 5,
  label_format = 30,
  font.size = 12,
  size = 5
)

Arguments

x

enrichment result.
color

selected variable to color the dots
xintercept

value to set x-intercept
yintercept

value to set y-intercept
showCategory

number of most significant enriched terms or selected terms to display determined by the variable selected to color the dots
label_format

a numeric value setting the wrap length, alternatively a custom function to format axis labels.
...

Additional parameters
font.size

font size for theme_dose()
size

font size to label selected categories specified by showCategory

Value

ggplot object

Author(s)

Guangchuang Yu

Examples

## Not run: 
library(DOSE)
data(geneList)
de <- names(geneList)[1:100]
x <- enrichDO(de)
volplot(x)

## End(Not run)