enrichplot

https://github.com/yulab-smu/enrichplot
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The 'enrichplot' package implements several ggplot2-based visualization methods for interpreting...
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### Install enrichplot from Bioconductor

Source: https://github.com/yulab-smu/enrichplot/blob/devel/README.md

Provides R code to install the released version of the enrichplot package using BiocManager. It first checks if BiocManager is installed and then proceeds with the installation.

```r
if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
## BiocManager::install("BiocUpgrade") ## you may need this
BiocManager::install("enrichplot")
```

--------------------------------

### Install enrichplot Development Version from GitHub

Source: https://github.com/yulab-smu/enrichplot/blob/devel/README.md

Demonstrates R code for installing the development version of the enrichplot package directly from GitHub using the 'remotes' package or 'yulab.utils'.

```r
## install.packages("remotes")
remotes::install_github("YuLab-SMU/enrichplot")

## or
## install.packages("yulab.utils")
yulab.utils::install_zip_gh("YuLab-SMU/enrichplot")
```

--------------------------------

### emapplot - Enrichment map for term similarity

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Creates network plots visualizing semantic similarity between enriched terms. Nodes represent terms, and edges represent their similarity. Supports clustering, grouping, and customization of appearance.

```APIDOC
## emapplot

### Description
Creates network plots showing enriched terms as nodes connected by edges representing semantic similarity between terms. Supports clustering and grouping of related terms.

### Method
R function

### Endpoint
N/A (R package function)

### Parameters
#### Path Parameters
N/A

#### Query Parameters
N/A

#### Request Body
N/A (Function arguments)
- **enrichment_result** (enrichResult/gseaResult) - The enrichment analysis result object. Should have pairwise term similarity calculated.
- **showCategory** (integer) - Maximum number of categories to display.
- **color** (string) - Variable to color the nodes by (e.g., 'p.adjust').
- **size_category** (numeric) - Factor to scale node size.
- **min_edge** (numeric) - Minimum edge weight for display.
- **node_label** (string) - Labeling of nodes ('category', 'term').
- **group** (logical) - Whether to group similar terms.
- **nCluster** (integer) - Number of clusters to form if grouping.
- **nWords** (integer) - Number of words to use for labeling clusters.
- **clusterFunction** (function) - Function to use for clustering.

### Request Example
```r
# Calculate pairwise term similarity first
enrichment_result <- pairwise_termsim(enrichment_result, method = "JC")

# Basic enrichment map
emapplot(enrichment_result, showCategory = 30)

# With clustering and labeling
emapplot(enrichment_result, showCategory = 30, color = "p.adjust", group = TRUE, nCluster = 5)
```

### Response
#### Success Response (200)
A network plot object (e.g., from igraph) representing the enrichment map.

#### Response Example
(A network plot object is returned, not a JSON/text response)
```

--------------------------------

### EMapplot for CompareCluster Results

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Generates an enrichment map visualization for compareCluster results. It visualizes term similarities and relationships, optionally grouping terms and coloring edges.

```r
compare_result <- pairwise_termsim(compare_result)
emapplot(compare_result,
         showCategory = 20,
         pie = "Count",
         group = TRUE,
         color_edge = "grey",
         size_edge = 0.5)
```

--------------------------------

### dotplot - Dot plot for enrichment visualization

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Generates dot plots where point size and color intensity represent enrichment statistics such as gene count, gene ratio, and statistical significance. Ideal for comparing multiple enrichment results.

```APIDOC
## dotplot

### Description
Generates dot plots where point size represents gene count or gene ratio, and color intensity indicates statistical significance. Excellent for comparing multiple enrichment results.

### Method
R function

### Endpoint
N/A (R package function)

### Parameters
#### Path Parameters
N/A

#### Query Parameters
N/A

#### Request Body
N/A (Function arguments)
- **enrichment_result** (enrichResult/gseaResult/compareClusterResult) - The enrichment analysis result object.
- **x** (string) - Variable to map to the x-axis (e.g., 'GeneRatio', 'Count').
- **color** (string) - Variable to map to the color of the points (e.g., 'p.adjust', 'NES').
- **size** (string) - Variable to map to the size of the points (e.g., 'Count', 'GeneRatio').
- **showCategory** (integer) - Number of top categories to display.
- **font.size** (numeric) - Font size for labels.
- **orderBy** (string) - Ordering variable for categories.
- **decreasing** (logical) - Whether to sort in decreasing order.
- **by** (string) - Grouping variable for compareCluster results.
- **includeAll** (logical) - Whether to include all categories in compareCluster results.

### Request Example
```r
# Basic dot plot
dotplot(enrichment_result, showCategory = 20)

# For GSEA results, color by NES (Normalized Enrichment Score)
gsea_result <- gseGO(geneList, OrgDb = org.Hs.eg.db, ont = "BP")
dotplot(gsea_result, color = "NES", showCategory = 20)
```

### Response
#### Success Response (200)
A ggplot object representing the dot plot visualization.

#### Response Example
(A ggplot object is returned, not a JSON/text response)
```

--------------------------------

### Dot Plot for Enrichment Visualization using R

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Creates dot plots to visualize enrichment results, where dot size indicates gene count or gene ratio, and color intensity reflects statistical significance. This method is effective for comparing multiple enrichment results and can be customized by the x-axis, color, size, and sorting order. It also supports GSEA results using NES and comparative cluster results.

```r
# Basic dot plot
dotplot(enrichment_result, showCategory = 20)

# Customize with gene ratio and count
dotplot(enrichment_result,
        x = "GeneRatio",
        color = "p.adjust",
        size = "Count",
        showCategory = 15,
        font.size = 12,
        orderBy = "x",
        decreasing = TRUE)

# For GSEA results, color by NES (Normalized Enrichment Score)
gsea_result <- gseGO(geneList, OrgDb = org.Hs.eg.db, ont = "BP")
dotplot(gsea_result, color = "NES", showCategory = 20)

# Compare clusters with grouping
compare_result <- compareCluster(geneClusters,
                                fun = "enrichGO",
                                OrgDb = org.Hs.eg.db)
dotplot(compare_result,
        showCategory = 10,
        by = "geneRatio",
        includeAll = TRUE,
        font.size = 10)
```

--------------------------------

### Treeplot for Hierarchical Clustering of Enriched Terms

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Visualizes enriched terms as a hierarchical tree based on similarity. It supports clustering, grouping, and customizable panels like heatmaps or pie charts.

```r
# Calculate term similarity
enrichment_result <- pairwise_termsim(enrichment_result)

# Basic tree plot
treeplot(enrichment_result, showCategory = 30)

# With hierarchical clustering and group labels
treeplot(enrichment_result,
         showCategory = 30,
         nCluster = 5,
         color = "p.adjust",
         hclust_method = "ward.D",
         cluster.params = list(
           method = "ward.D",
           n = 5,
           label_words_n = 4,
           label_format = 30
         ),
         hilight.params = list(
           hilight = TRUE,
           align = "both"
         ))

# For compareCluster with heatmap panel
compare_result <- pairwise_termsim(compare_result)
treeplot(compare_result,
         showCategory = 20,
         nCluster = 4,
         clusterPanel.params = list(
           clusterPanel = "heatMap",
           colnames_angle = 45
         ))

# With pie chart panel
treeplot(compare_result,
         showCategory = 15,
         clusterPanel.params = list(
           clusterPanel = "pie",
           pie = "Count",
           legend_n = 3
         ))
```

--------------------------------

### Gene Ontology DAG Plot: goplot

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Visualizes Gene Ontology (GO) term relationships as a directed acyclic graph (DAG), illustrating hierarchical structure and ancestor-descendant relationships. Supports customization of labels and layout.

```r
# Perform GO enrichment
go_result <- enrichGO(de_genes,
                     OrgDb = org.Hs.eg.db,
                     ont = "BP",
                     pvalueCutoff = 0.05)

# GO DAG plot with text labels
goplot(go_result,
       showCategory = 10,
       color = "p.adjust",
       layout = igraph::layout_with_sugiyama,
       geom = "text")

# With label style
goplot(go_result,
       showCategory = 8,
       color = "pvalue",
       geom = "label")
```

--------------------------------

### Generate GSEA Plots and Customize

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Functions for visualizing Gene Set Enrichment Analysis (GSEA) results. Includes horizontal plots (hplot), detailed plots with specific gene highlighting (gseaplot2 with geom_gsea_gene), and distribution plots (gseadist). Supports customization of gene sets displayed and gene annotations.

```r
# Horizontal GSEA plot
hplot(gsea_result, geneSetID = 1:5)

# Label specific genes in GSEA plot
gseaplot2(gsea_result, geneSetID = 1) +
  geom_gsea_gene(genes = c("TP53", "BRCA1", "MYC"))

# Distribution plot for GSEA
gseadist(gsea_result, geneSetID = 1:3)
```

--------------------------------

### barplot - Bar chart visualization for enrichment results

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Generates horizontal bar charts to display enriched terms, with options for customization of colors, axis, and displayed categories. Suitable for both single and comparative enrichment analyses.

```APIDOC
## barplot

### Description
Creates horizontal bar charts showing enriched terms with customizable colors based on statistical significance. Supports both single enrichment analyses and comparative cluster results.

### Method
R function

### Endpoint
N/A (R package function)

### Parameters
#### Path Parameters
N/A

#### Query Parameters
N/A

#### Request Body
N/A (Function arguments)
- **enrichment_result** (enrichResult/gseaResult/compareClusterResult) - The enrichment analysis result object.
- **x** (string) - 'GeneRatio' or 'Count' to specify the x-axis.
- **color** (string) - Variable to color the bars by (e.g., 'p.adjust', 'pvalue', 'qvalue', 'GeneRatio', 'Count').
- **showCategory** (integer or character vector) - Number of top categories to display or specific category names.
- **font.size** (numeric) - Font size for labels.
- **title** (string) - Title of the plot.

### Request Example
```r
library(DOSE)
library(enrichplot)
data(geneList)
de_genes <- names(geneList)[1:100]
enrichment_result <- enrichDO(de_genes)
barplot(enrichment_result, showCategory = 10)
```

### Response
#### Success Response (200)
A ggplot object representing the bar chart visualization.

#### Response Example
(A ggplot object is returned, not a JSON/text response)
```

--------------------------------

### Enrichment Map for Term Similarity using R (emapplot)

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Generates network plots where enriched terms are nodes and edges represent semantic similarity. This visualization aids in understanding the relationships and potential clustering of related terms. The plot can be customized with coloring by statistical significance, node sizing, edge filtering, and grouping of terms with options for clustering algorithms.

```r
# Calculate pairwise term similarity first
enrichment_result <- pairwise_termsim(enrichment_result, method = "JC")

# Basic enrichment map
emapplot(enrichment_result, showCategory = 30)

# With clustering and labeling
emapplot(enrichment_result,
         showCategory = 30,
         color = "p.adjust",
         size_category = 1,
         min_edge = 0.2,
         node_label = "category",
         group = TRUE,
         nCluster = 5,
         nWords = 4,
         clusterFunction = stats::kmeans)
```

--------------------------------

### Customize enrichplot Color Palette

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Allows users to set custom color palettes for enrichplot visualizations. The `set_enrichplot_color` function enables defining fill colors based on specified types and color vectors, facilitating consistent aesthetic choices across plots.

```r
set_enrichplot_color(type = "fill",
                    colors = c("#327eba", "white", "#e06663"))
```

--------------------------------

### GSEA Plots: gseaplot and gseaplot2

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Visualizes Gene Set Enrichment Analysis (GSEA) results, showing the running enrichment score and gene rank positions. gseaplot provides a classic view, while gseaplot2 offers more modern, customizable multi-subplot layouts.

```r
# Perform GSEA
gsea_result <- gseGO(geneList,
                     OrgDb = org.Hs.eg.db,
                     ont = "BP",
                     pvalueCutoff = 0.05)

# Classic GSEA plot
gseaplot(gsea_result, geneSetID = 1, title = "Top Enriched Pathway")

# Modern GSEA plot with multiple subplots
gseaplot2(gsea_result,
          geneSetID = 1:3,
          title = "Top 3 Pathways",
          color = c("green", "red", "blue"),
          base_size = 11,
          rel_heights = c(1.5, 0.5, 1),
          subplots = 1:3,
          pvalue_table = TRUE)

# Show only running score
gseaplot2(gsea_result,
          geneSetID = 1,
          subplots = 1,
          ES_geom = "line")

# Rank plot
gsearank(gsea_result, geneSetID = 1, title = "Gene Ranking")
```

--------------------------------

### Create and Combine Enrichment Plots

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Generates various standard enrichment plots such as dot plots (dotplot) and bar plots (barplot) from enrichment results. The `plot_list` function allows combining multiple ggplot objects into a single visualization with a specified layout (e.g., number of columns), useful for creating comprehensive reports.

```r
p1 <- dotplot(enrichment_result)
p2 <- barplot(enrichment_result)
plot_list(gglist = list(p1, p2), ncol = 2)
```

--------------------------------

### UpSet Plot for Gene Overlaps: upsetplot

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Generates UpSet plots to visualize gene overlaps between enriched terms, which is more scalable than Venn diagrams for multiple sets. Supports different visualization types like boxplots or violin plots for GSEA results.

```r
# UpSet plot for enrichment result
upsetplot(enrichment_result, n = 10)

# For GSEA results with distribution visualization
upsetplot(gsea_result,
          n = 15,
          type = "boxplot")

# With violin plot
upsetplot(gsea_result,
          n = 12,
          type = "violin")
```

--------------------------------

### Ridge Plot for GSEA Gene Distribution: ridgeplot

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Displays the distribution of gene expression values across enriched pathways using overlapping density plots. It allows customization of ordering, coloring, and labeling.

```r
# Basic ridge plot
ridgeplot(gsea_result, showCategory = 20)

# Customize ordering and coloring
ridgeplot(gsea_result,
          showCategory = 15,
          fill = "p.adjust",
          core_enrichment = TRUE,
          orderBy = "NES",
          decreasing = FALSE,
          label_format = 30)

# Use custom labeling function
ridgeplot(gsea_result,
          showCategory = 10,
          fill = "pvalue",
          label_format = function(x) gsub("_", " ", x))
```

--------------------------------

### Calculate Pairwise Term Similarity

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Computes pairwise similarity between enriched terms using specified methods (e.g., JC for Jaccard, Wang for semantic similarity). This is crucial for visualizing functional relationships in networks or trees. It accepts enrichment results from various sources and allows control over the number of categories shown.

```r
enrichment_result <- pairwise_termsim(enrichment_result,
                                     method = "JC",
                                     showCategory = 200)

go_result <- pairwise_termsim(go_result,
                              method = "Wang",
                              semData = NULL,
                              showCategory = 100)

compare_result <- pairwise_termsim(compare_result,
                                  method = "JC",
                                  showCategory = 150)
```

--------------------------------

### Heatmap of Enriched Terms and Genes: heatplot

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Creates heatmaps to visualize the relationship between enriched terms and genes. It supports encoding fold change and p-value, with options for different symbols (rectangles or dots).

```r
# Basic heatmap
heatplot(enrichment_result, showCategory = 20)

# With fold change coloring
heatplot(enrichment_result,
         showCategory = 15,
         foldChange = fold_changes,
         symbol = "rect",
         label_format = 30)

# Dot-style with both fold change and p-value
gene_pvalues <- runif(100, 0, 0.05)
names(gene_pvalues) <- names(fold_changes)

heatplot(enrichment_result,
         showCategory = 20,
         foldChange = fold_changes,
         pvalue = gene_pvalues,
         symbol = "dot",
         label_format = function(x) stringr::str_wrap(x, width = 40))
```

--------------------------------

### Bar Plot for Enrichment Results using R

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Generates horizontal bar charts for enriched terms. It visualizes the number of genes associated with each term and can be customized by color based on statistical significance (e.g., p.adjust) and the x-axis representation (e.g., GeneRatio). It supports displaying a specified number of categories or a list of specific terms.

```r
library(DOSE)
library(enrichplot)

# Load example data
data(geneList)
de_genes <- names(geneList)[1:100]

# Perform enrichment analysis
enrichment_result <- enrichDO(de_genes)

# Basic bar plot showing gene count
barplot(enrichment_result, showCategory = 10)

# Color by adjusted p-value with gene ratio on x-axis
barplot(enrichment_result,
        x = "GeneRatio",
        color = "p.adjust",
        showCategory = 15,
        font.size = 12,
        title = "Disease Ontology Enrichment")

# Select specific categories to display
selected_terms <- c("urinary bladder cancer",
                   "bronchiolitis obliterans",
                   "aortic aneurysm")
barplot(enrichment_result, showCategory = selected_terms)
```

--------------------------------

### cnetplot - Category-gene network plot

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Visualizes the relationships between enriched terms and their associated genes as a network graph. Supports customization of node labels, colors, and layout, and can incorporate fold change information.

```APIDOC
## cnetplot

### Description
Visualizes the relationship between enriched terms and their associated genes as a network graph, with optional fold change coloring.

### Method
R function

### Endpoint
N/A (R package function)

### Parameters
#### Path Parameters
N/A

#### Query Parameters
N/A

#### Request Body
N/A (Function arguments)
- **enrichment_result** (enrichResult/gseaResult/compareClusterResult) - The enrichment analysis result object.
- **showCategory** (integer) - Number of top categories to display.
- **foldChange** (numeric vector) - Named vector of fold change values for genes.
- **node_label** (string) - Labeling of nodes ('all', 'category', 'gene').
- **color_category** (string) - Color for category nodes.
- **color_edge** (string) - Color for edges.
- **layout** (function) - Layout function from igraph.
- **hilight** (character vector) - Terms or genes to highlight.
- **hilight_alpha** (numeric) - Transparency for highlighted elements.
- **pie** (string) - Variable to represent as pie chart slices (e.g., 'Count').

### Request Example
```r
# Basic category-gene network
cnetplot(enrichment_result, showCategory = 5)

# Add fold change information
fold_changes <- geneList[1:100]
cnetplot(enrichment_result, showCategory = 5, foldChange = fold_changes, node_label = "all")

# For compareCluster results with pie charts
cnetplot(compare_result, showCategory = 8, pie = "Count", node_label = "category")
```

### Response
#### Success Response (200)
A network plot object (e.g., from igraph) representing the category-gene network.

#### Response Example
(A network plot object is returned, not a JSON/text response)
```

--------------------------------

### Category-Gene Network Plot using R (cnetplot)

Source: https://context7.com/yulab-smu/enrichplot/llms.txt

Visualizes the relationships between enriched terms and their associated genes as a network graph. The plot can optionally display fold change information by coloring nodes and edges. It supports various layout algorithms and can highlight specific categories or display pie charts for comparing clusters.

```r
# Basic category-gene network
cnetplot(enrichment_result, showCategory = 5)

# Add fold change information
fold_changes <- geneList[1:100]
cnetplot(enrichment_result,
         showCategory = 5,
         foldChange = fold_changes,
         node_label = "all",
         color_category = "#E5C494",
         color_edge = "grey",
         layout = igraph::layout_with_kk)

# For compareCluster results with pie charts
cnetplot(compare_result,
         showCategory = 8,
         pie = "Count",
         node_label = "category",
         layout = igraph::layout_with_fr)

# Highlight specific categories
cnetplot(enrichment_result,
         showCategory = 10,
         foldChange = fold_changes,
         hilight = c("GO:0008150", "GO:0005575"),
         hilight_alpha = 0.3)
```
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