### Calculate Similarity Matrix

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/calculateSimMatrix.html

This example demonstrates how to calculate a similarity matrix for GO terms using the 'Rel' method and the 'org.Hs.eg.db' database for biological process (BP) ontology. It first reads example GO IDs from a file and then calls calculateSimMatrix.

```R
go_analysis <- read.delim(system.file("extdata/example.txt", package="rrvgo"))
simMatrix <- calculateSimMatrix(go_analysis$ID, orgdb="org.Hs.eg.db", ont="BP", method="Rel")
#> 
#> preparing gene to GO mapping data...
#> preparing IC data...
>
```

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

### Launch Shiny Application

Source: https://context7.com/ssayols/rrvgo/llms.txt

Starts an interactive web interface for the rrvgo workflow. Supports custom host and port configurations.

```r
library(rrvgo)

# Launch the interactive Shiny application
shiny_rrvgo()

# Run on a specific port
shiny_rrvgo(port = 3838)

# Run with specific host for network access
shiny_rrvgo(host = "0.0.0.0", port = 3838)

# Launch in browser automatically
shiny_rrvgo(launch.browser = TRUE)
```

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

### Install rrvgo package

Source: https://github.com/ssayols/rrvgo/blob/master/README.md

Use BiocManager to install the rrvgo package from Bioconductor.

```r
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("rrvgo")
```

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

### Plot GO terms as a treemap using treemapPlot

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/treemapPlot.html

Use treemapPlot to visualize reduced GO terms. Ensure the 'rrvgo' package is installed and necessary data is loaded. This example demonstrates a typical workflow from reading data to plotting.

```R
if (FALSE) { # \dontrun{
go_analysis <- read.delim(system.file("extdata/example.txt", package="rrvgo"))
simMatrix <- calculateSimMatrix(go_analysis$ID, orgdb="org.Hs.eg.db", ont="BP", method="Rel")
scores <- setNames(-log10(go_analysis$qvalue), go_analysis$ID)
reducedTerms <- reduceSimMatrix(simMatrix, scores, threshold=0.7, orgdb="org.Hs.eg.db")
treemapPlot(reducedTerms)
} # }
```

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

### Prepare Data for rrvgo Plots

Source: https://context7.com/ssayols/rrvgo/llms.txt

Loads GO analysis data, calculates semantic similarity matrix, computes GO term scores (e.g., -log10(qvalue)), and reduces redundant terms based on a similarity threshold. Ensure 'org.Hs.eg.db' is installed for human annotations.

```r
library(rrvgo)

# Prepare data
go_analysis <- read.delim(system.file("extdata/example.txt", package="rrvgo"))
simMatrix <- calculateSimMatrix(go_analysis$ID, orgdb="org.Hs.eg.db", ont="BP", method="Rel")
scores <- setNames(-log10(go_analysis$qvalue), go_analysis$ID)
reducedTerms <- reduceSimMatrix(simMatrix, scores, threshold=0.7, orgdb="org.Hs.eg.db")
```

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

### Get GO Term Description

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/getGoTerm.html

Retrieves the description of a specified GO term.

```APIDOC
## GET /go/term/{id}

### Description
Retrieves the description of a GO term using its identifier.

### Method
GET

### Endpoint
/go/term/{id}

### Parameters
#### Path Parameters
- **id** (string) - Required - The GO term identifier (e.g., GO:0008150).

### Response
#### Success Response (200)
- **term** (string) - The description of the GO term.

#### Response Example
```json
{
  "term": "biological_process"
}
```
```

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

### Generate ggplot2-style colors

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/gg_color_hue.html

Use this function to create a vector of colors based on the ggplot2 hue palette. The example is wrapped in a dontrun block.

```R
if (FALSE) { # \dontrun{
plot(1:10, pch=16, cex=2, col=gg_color_hue(10))
} # }
```

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

### Get GO Term Size

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/getGoSize.html

Retrieves the number of genes associated with specified GO terms.

```APIDOC
## GET /getGoSize

### Description
Retrieves the number of genes associated with GO terms.

### Method
GET

### Endpoint
/getGoSize

### Parameters
#### Query Parameters
- **terms** (GO terms) - Required - GO terms to query.
- **orgdb** (string) - Required - Bioconductor org.* package name.
- **keytype** (string) - Optional - Keytype for AnnotationDbi::keys.
- **children** (boolean) - Optional - Whether to include genes in children terms.

### Response
#### Success Response (200)
- **genes_count** (integer) - The number of genes associated with each term.

#### Response Example
{
  "genes_count": 150
}
```

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

### Get Unique Parent Terms

Source: https://context7.com/ssayols/rrvgo/llms.txt

Extracts and counts unique parent terms (cluster representatives) from the reducedTerms data frame. Use this to understand the number of distinct clusters identified.

```R
parent_terms <- unique(reducedTerms$parentTerm)
cat("Number of clusters:", length(parent_terms), "\n")
cat("Original terms:", nrow(reducedTerms), "\n")
```

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

### Launch the Shiny application

Source: https://github.com/ssayols/rrvgo/blob/master/docs/articles/rrvgo.html

Opens an interactive interface for exploring rrvgo plots and tables.

```R
rrvgo::shiny_rrvgo()
```

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

### Initialize custom OrgDb data

Source: https://github.com/ssayols/rrvgo/blob/master/docs/articles/rrvgo.html

Prepares semantic similarity data for organisms not natively supported by Bioconductor.

```R
my_new_fancy_orgdb_object <- 'org.Zz.eg.db'
hsGO <- GOSemSim::godata(my_new_fancy_orgdb_object, ont="MF")
```

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

### shiny_rrvgo

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/shiny_rrvgo.html

Launches an interactive web interface for rrvgo.

```APIDOC
## shiny_rrvgo

### Description
Launch an interactive web interface for the rrvgo package.

### Method
Function Call

### Parameters
#### Arguments
- **...** (dots) - Optional - Other parameters sent to shiny::runApp().

### Response
- **Value** (null) - Nothing
```

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

### Create Basic Heatmap with Parent Annotations

Source: https://context7.com/ssayols/rrvgo/llms.txt

Displays the GO term similarity matrix as a clustered heatmap with annotations indicating parent terms. This helps visualize term relationships and cluster assignments.

```r
heatmapPlot(
  simMatrix = simMatrix,
  reducedTerms = reducedTerms,
  annotateParent = TRUE,        # Show cluster annotations
  annotationLabel = "parentTerm" # Use term names (or "parent" for GO IDs)
)
```

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

### Launch Interactive Web Interface with shiny_rrvgo

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/shiny_rrvgo.html

Use this function to launch an interactive web interface. It accepts additional parameters that are passed to shiny::runApp().

```R
shiny_rrvgo(...)
```

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

### Generate Word Cloud Plots

Source: https://context7.com/ssayols/rrvgo/llms.txt

Creates word clouds from GO term descriptions. Requires a reduced term set from reduceSimMatrix.

```r
library(rrvgo)

# Prepare data
go_analysis <- read.delim(system.file("extdata/example.txt", package="rrvgo"))
simMatrix <- calculateSimMatrix(go_analysis$ID, orgdb="org.Hs.eg.db", ont="BP", method="Rel")
scores <- setNames(-log10(go_analysis$qvalue), go_analysis$ID)
reducedTerms <- reduceSimMatrix(simMatrix, scores, threshold=0.7, orgdb="org.Hs.eg.db")

# Basic word cloud using only parent terms
wordcloudPlot(
  reducedTerms = reducedTerms,
  min.freq = 1,                 # Minimum word frequency to include
  colors = "black"              # Single color for all words
)

# Use all terms (not just parents) for word frequencies
wordcloudPlot(
  reducedTerms = reducedTerms,
  onlyParents = FALSE,          # Include all terms
  min.freq = 2
)

# Colorful word cloud with more customization
wordcloudPlot(
  reducedTerms = reducedTerms,
  min.freq = 1,
  colors = RColorBrewer::brewer.pal(8, "Dark2"),
  max.words = 50,               # Limit number of words
  random.order = FALSE,         # Most frequent words in center
  scale = c(3, 0.5)             # Font size range
)

# Save to file
png("go_wordcloud.png", width=600, height=600)
wordcloudPlot(reducedTerms, min.freq=1, colors="black")
dev.off()
```

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

### shiny_rrvgo

Source: https://context7.com/ssayols/rrvgo/llms.txt

Launches an interactive Shiny web application for the complete RRVGO workflow.

```APIDOC
## shiny_rrvgo

### Description
Launches an interactive Shiny web application that provides a graphical interface for the complete RRVGO workflow.

### Parameters
#### Request Body
- **port** (numeric) - Optional - Port to run the application on.
- **host** (character) - Optional - Host address for network access.
- **launch.browser** (logical) - Optional - Whether to launch in the browser automatically.
```

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

### Access rrvgo documentation

Source: https://github.com/ssayols/rrvgo/blob/master/README.md

Open the package vignettes within the R environment.

```r
browseVignettes("rrvgo")
```

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

### Retrieve GO Term Descriptions

Source: https://context7.com/ssayols/rrvgo/llms.txt

Fetches human-readable descriptions for specific GO IDs from the annotation database.

```r
library(rrvgo)

# Get term descriptions for GO IDs
go_ids <- c("GO:0006119", "GO:0042775", "GO:0022904")
terms <- getGoTerm(go_ids)

print(terms)
```

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

### getGoTerm

Source: https://context7.com/ssayols/rrvgo/llms.txt

Retrieves the human-readable term description for one or more GO IDs.

```APIDOC
## getGoTerm

### Description
Retrieves the human-readable term description for one or more GO IDs from the GO.db annotation database.

### Parameters
#### Request Body
- **go_ids** (character vector) - Required - A vector of GO IDs.
```

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

### Generate a word cloud plot

Source: https://github.com/ssayols/rrvgo/blob/master/docs/articles/rrvgo.html

Creates a word cloud visualization to emphasize frequently appearing GO terms.

```R
wordcloudPlot(reducedTerms, min.freq=1, colors="black")
```

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

### Generate Heatmap Visualization

Source: https://context7.com/ssayols/rrvgo/llms.txt

Creates a heatmap of the similarity matrix, optionally annotating parent terms. Useful for visualizing the similarity structure between GO terms. Set fontsize for labels.

```R
heatmapPlot(simMatrix, reducedTerms, annotateParent=TRUE,
            annotationLabel="parentTerm", fontsize=6)
```

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

### Execute Complete Workflow

Source: https://context7.com/ssayols/rrvgo/llms.txt

Performs the full pipeline from loading enrichment results to calculating similarity and reducing terms.

```r
library(rrvgo)

# Step 1: Load GO enrichment results
# Expected format: tab-delimited file with at least 'ID' column (GO terms)
# Optional: score column (e.g., p-value, q-value, fold enrichment)
go_analysis <- read.delim(system.file("extdata/example.txt", package="rrvgo"))

# Inspect the data
head(go_analysis)

# Step 2: Calculate similarity matrix
simMatrix <- calculateSimMatrix(
  x = go_analysis$ID,
  orgdb = "org.Hs.eg.db",       # Human
  ont = "BP",                   # Biological Process
  method = "Rel"                # Relevance method
)

# Step 3: Prepare scores (transform p-values: higher = better)
scores <- setNames(-log10(go_analysis$qvalue), go_analysis$ID)

# Step 4: Reduce similar terms
reducedTerms <- reduceSimMatrix(
  simMatrix = simMatrix,
  scores = scores,
  threshold = 0.7,              # Medium similarity threshold
  orgdb = "org.Hs.eg.db"
)

# Step 5: Explore the results
```

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

### Visualize GO terms as treemap

Source: https://github.com/ssayols/rrvgo/blob/master/docs/articles/rrvgo.html

Creates a treemap visualization where term grouping is based on parent terms and size is proportional to scores.

```R
treemapPlot(reducedTerms)
```

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

### Retrieve package citation

Source: https://github.com/ssayols/rrvgo/blob/master/docs/articles/rrvgo.html

Displays the citation information for the rrvgo package.

```R
citation("rrvgo")
```

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

### wordcloudPlot

Source: https://context7.com/ssayols/rrvgo/llms.txt

Generates a word cloud from GO term descriptions to identify common themes in enrichment results.

```APIDOC
## wordcloudPlot

### Description
Generates a word cloud from GO term descriptions, highlighting frequently occurring words across the reduced term set.

### Parameters
#### Request Body
- **reducedTerms** (object) - Required - The result object from reduceSimMatrix.
- **min.freq** (numeric) - Optional - Minimum word frequency to include.
- **colors** (character/vector) - Optional - Color palette for the words.
- **onlyParents** (logical) - Optional - Whether to use only parent terms (default TRUE).
- **max.words** (numeric) - Optional - Limit the number of words displayed.
- **random.order** (logical) - Optional - If FALSE, most frequent words are in the center.
- **scale** (numeric vector) - Optional - Font size range.
```

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

### Calculate GO term similarity matrix

Source: https://github.com/ssayols/rrvgo/blob/master/docs/articles/rrvgo.html

Computes the semantic similarity matrix for a list of GO terms using a specified organism database and ontology.

```R
library(rrvgo)
go_analysis <- read.delim(system.file("extdata/example.txt", package="rrvgo"))
simMatrix <- calculateSimMatrix(go_analysis$ID,
                                orgdb="org.Hs.eg.db",
                                ont="BP",
                                method="Rel")
```

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

### Reduce GO Terms by Similarity and Scores

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/reduceSimMatrix.html

Use reduceSimMatrix to group GO terms that are similar above a specified threshold. Scores can be provided to determine the representative term for each group, otherwise 'uniqueness' or 'size' are used. Requires a pre-computed similarity matrix and an OrgDb package.

```R
go_analysis <- read.delim(system.file("extdata/example.txt", package="rrvgo"))
simMatrix <- calculateSimMatrix(go_analysis$ID, orgdb="org.Hs.eg.db", ont="BP", method="Rel")
#> preparing gene to GO mapping data...
#> preparing IC data...
scores <- setNames(-log10(go_analysis$qvalue), go_analysis$ID)
reducedTerms <- reduceSimMatrix(simMatrix, scores, threshold=0.7, orgdb="org.Hs.eg.db")
#> 'select()' returned 1:many mapping between keys and columns
```

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

### Create Simple Heatmap Without Annotations

Source: https://context7.com/ssayols/rrvgo/llms.txt

Generates a basic heatmap of the similarity matrix without any parent term annotations.

```r
heatmapPlot(
  simMatrix = simMatrix,
  annotateParent = FALSE
)
```

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

### Generate Word Cloud of Reduced GO Terms

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/wordcloudPlot.html

Use this function to visualize reduced GO terms as a word cloud. Ensure the 'reducedTerms' argument is a data frame generated by reduceSimMatrix(). Additional parameters can be passed to customize the word cloud appearance.

```R
go_analysis <- read.delim(system.file("extdata/example.txt", package="rrvgo"))
simMatrix <- calculateSimMatrix(go_analysis$ID, orgdb="org.Hs.eg.db", ont="BP", method="Rel")
#> preparing gene to GO mapping data...
#> preparing IC data...
scores <- setNames(-log10(go_analysis$qvalue), go_analysis$ID)
reducedTerms <- reduceSimMatrix(simMatrix, scores, threshold=0.7, orgdb="org.Hs.eg.db")
#> 'select()' returned 1:many mapping between keys and columns
wordcloudPlot(reducedTerms, min.freq=1, colors="black")
```

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

### Generate Scatter Plot Visualization

Source: https://context7.com/ssayols/rrvgo/llms.txt

Creates a scatter plot to visualize term relationships using PCA. Requires the similarity matrix and reduced terms data. Adjust labelSize for readability.

```R
p1 <- scatterPlot(simMatrix, reducedTerms, algorithm="pca", labelSize=3)
print(p1)
```

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

### calculateSimMatrix

Source: https://context7.com/ssayols/rrvgo/llms.txt

Calculates a pairwise semantic similarity matrix for a set of GO terms using specified methods and organism databases.

```APIDOC
## calculateSimMatrix

### Description
Calculates a semantic similarity matrix between GO terms using one of five established methods (Resnik, Lin, Rel, Jiang, Wang) from the GOSemSim package.

### Parameters
#### Arguments
- **x** (vector) - Required - Vector of GO term IDs.
- **orgdb** (string) - Required - Organism annotation database (e.g., "org.Hs.eg.db").
- **ont** (string) - Required - Ontology: "BP", "MF", or "CC".
- **method** (string) - Required - Semantic similarity method: "Resnik", "Lin", "Rel", "Jiang", or "Wang".
- **semdata** (object) - Optional - Pre-computed semantic data object.
- **include_terms_without_IC** (boolean) - Optional - Whether to include terms without Information Content.

### Response
- **simMatrix** (matrix) - A square similarity matrix with values between 0 and 1, where rownames and colnames are GO term IDs.
```

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

### Visualize similarity matrix as heatmap

Source: https://github.com/ssayols/rrvgo/blob/master/docs/articles/rrvgo.html

Generates a heatmap of the similarity matrix with hierarchical clustering of terms.

```R
heatmapPlot(simMatrix,
            reducedTerms,
            annotateParent=TRUE,
            annotationLabel="parentTerm",
            fontsize=6)
```

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

### Save Heatmap to File

Source: https://context7.com/ssayols/rrvgo/llms.txt

Exports a similarity heatmap to a PNG file with specified dimensions.

```r
png("similarity_heatmap.png", width=1000, height=1000)
heatmapPlot(simMatrix, reducedTerms, annotateParent=TRUE, fontsize=6)
dev.off()
```

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

### Plot GO Terms with scatterPlot

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/scatterPlot.html

Use scatterPlot to visualize GO terms as scattered points. This function requires a similarity matrix and reduced terms data frame. Point size can be set to 'score' or 'size'.

```R
go_analysis <- read.delim(system.file("extdata/example.txt", package="rrvgo"))
simMatrix <- calculateSimMatrix(go_analysis$ID, orgdb="org.Hs.eg.db", ont="BP", method="Rel")
#> preparing gene to GO mapping data...
#> preparing IC data...
scores <- setNames(-log10(go_analysis$qvalue), go_analysis$ID)
reducedTerms <- reduceSimMatrix(simMatrix, scores, threshold=0.7, orgdb="org.Hs.eg.db")
#> 'select()' returned 1:many mapping between keys and columns
scatterPlot(simMatrix, reducedTerms)
#> Warning: ggrepel: 10 unlabeled data points (too many overlaps). Consider increasing max.overlaps
```

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

### Visualization Functions

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/index.html

Functions for plotting GO term analysis results.

```APIDOC
## Visualization Functions

### Description
Functions to visualize GO term data using various plot types.

### Methods
- heatmapPlot(): Plot similarity matrix as a heatmap
- scatterPlot(): Plot GO terms as scattered points
- treemapPlot(): Plot GO terms as a treemap
- wordcloudPlot(): Plot GO reduced terms as a wordcloud
```

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

### Annotate Heatmap with GO IDs

Source: https://context7.com/ssayols/rrvgo/llms.txt

Generates a heatmap annotated with GO IDs instead of human-readable parent term names, useful for precise identification.

```r
heatmapPlot(
  simMatrix = simMatrix,
  reducedTerms = reducedTerms,
  annotateParent = TRUE,
  annotationLabel = "parent",   # Show GO IDs like "GO:0006119"
  fontsize = 8
)
```

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

### Calculate Semantic Similarity Matrix in R

Source: https://context7.com/ssayols/rrvgo/llms.txt

Computes pairwise similarity scores for GO terms using methods like Resnik, Lin, or Relevance. Pre-computing semdata can improve performance for repeated calculations.

```r
library(rrvgo)

# Load example GO enrichment results
go_analysis <- read.delim(system.file("extdata/example.txt", package="rrvgo"))

# Calculate similarity matrix for Biological Process ontology using Relevance method
# Supported ontologies: "BP" (Biological Process), "MF" (Molecular Function), "CC" (Cellular Component)
# Supported methods: "Resnik", "Lin", "Rel", "Jiang", "Wang"
simMatrix <- calculateSimMatrix(
  x = go_analysis$ID,           # Vector of GO term IDs (e.g., "GO:0006119", "GO:0042775")
  orgdb = "org.Hs.eg.db",       # Organism annotation database (Human)
  ont = "BP",                   # Ontology: BP, MF, or CC
  method = "Rel"                # Semantic similarity method
)

# For better performance when running multiple times, pre-compute semdata
semdata <- GOSemSim::godata("org.Hs.eg.db", ont = "BP", keytype = "ENTREZID")
simMatrix <- calculateSimMatrix(
  x = go_analysis$ID,
  orgdb = "org.Hs.eg.db",
  semdata = semdata,            # Reuse pre-computed semantic data
  ont = "BP",
  method = "Rel"
)

# Include terms without Information Content (use with caution)
simMatrix <- calculateSimMatrix(
  x = go_analysis$ID,
  orgdb = "org.Hs.eg.db",
  ont = "BP",
  method = "Rel",
  include_terms_without_IC = TRUE
)

# Result: Square similarity matrix with values between 0 and 1
# rownames and colnames are GO term IDs
print(simMatrix[1:3, 1:3])
#              GO:0006119 GO:0042775 GO:0042773
# GO:0006119   1.0000000  0.9234567  0.8765432
# GO:0042775   0.9234567  1.0000000  0.8901234
# GO:0042773   0.8765432  0.8901234  1.0000000
```

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

### Plot Similarity Matrix as Heatmap with heatmapPlot

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/heatmapPlot.html

Use this function to visualize a similarity matrix as a heatmap. It requires a square similarity matrix and optionally accepts reduced terms for annotation. Customization of parent term annotation and font size is supported.

```R
heatmapPlot(
  simMatrix,
  reducedTerms = NULL,
  annotateParent = TRUE,
  annotationLabel = "parentTerm",
  ...
)
```

```R
go_analysis <- read.delim(system.file("extdata/example.txt", package="rrvgo"))
simMatrix <- calculateSimMatrix(go_analysis$ID, orgdb="org.Hs.eg.db", ont="BP", method="Rel")
#> preparing gene to GO mapping data...
#> preparing IC data...
scores <- setNames(-log10(go_analysis$qvalue), go_analysis$ID)
reducedTerms <- reduceSimMatrix(simMatrix, scores, threshold=0.7, orgdb="org.Hs.eg.db")
#> 'select()' returned 1:many mapping between keys and columns
heatmapPlot(simMatrix, reducedTerms, annotateParent=TRUE, annotationLabel="parentTerm", fontsize=6)

```

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

### Generate Treemap Visualization

Source: https://context7.com/ssayols/rrvgo/llms.txt

Generates a treemap to show hierarchical grouping of GO terms, with area proportional to significance. Requires the reduced terms data and a title for the plot.

```R
treemapPlot(reducedTerms, title="GO Enrichment Summary")
```

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

### Reduce GO terms by similarity

Source: https://github.com/ssayols/rrvgo/blob/master/docs/articles/rrvgo.html

Groups GO terms based on a similarity threshold and selects a representative term for each group based on provided scores.

```R
scores <- setNames(-log10(go_analysis$qvalue), go_analysis$ID)
reducedTerms <- reduceSimMatrix(simMatrix,
                                scores,
                                threshold=0.7,
                                orgdb="org.Hs.eg.db")
```

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

### loadOrgdb Function

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/loadOrgdb.html

Loads an orgdb object from a specified Bioconductor org.* package.

```APIDOC
## loadOrgdb

### Description
Loads an orgdb object from a Bioconductor org.* package.

### Parameters
#### Arguments
- **orgdb** (string) - Required - The name of one of the org.* Bioconductor packages.

### Value
- **object** - The loaded orgdb object.
```

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

### Function: gg_color_hue

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/gg_color_hue.html

Generates a vector of colors based on the number of requested hues.

```APIDOC
## gg_color_hue

### Description
Emulate ggplot2 color palette by generating equally spaced hues around the color wheel.

### Parameters
#### Arguments
- **n** (integer) - Required - The number of colors to generate.

### Value
- **vector** (alphanumeric) - A vector containing the generated color codes.

### Request Example
```r
gg_color_hue(10)
```

### Response Example
```r
[1] "#F8766D" "#D39200" "#93AA00" "#00BA38" "#00C19F" "#00B9E3" "#619CFF" "#DB72FB" "#FF61C3"
```
```

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

### Visualize GO terms as scatter plot

Source: https://github.com/ssayols/rrvgo/blob/master/docs/articles/rrvgo.html

Plots GO terms using PCoA coordinates to represent distances based on similarity.

```R
scatterPlot(simMatrix, reducedTerms)
```

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

### reduceSimMatrix

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/index.html

Reduces a set of GO terms based on their semantic similarity and scores.

```APIDOC
## reduceSimMatrix

### Description
Reduce a set of GO terms based on their semantic similarity and scores.

### Method
Function Call

### Endpoint
reduceSimMatrix()
```

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

### Create Scatter Plot with UMAP

Source: https://context7.com/ssayols/rrvgo/llms.txt

Generates a scatter plot of GO terms using UMAP for dimensionality reduction. This is an alternative to PCA for visualizing semantic similarity.

```r
scatterPlot(
  simMatrix = simMatrix,
  reducedTerms = reducedTerms,
  algorithm = "umap"            # "pca" (default) or "umap"
)
```

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

### getGoSize

Source: https://context7.com/ssayols/rrvgo/llms.txt

Retrieves the number of genes associated with GO terms.

```APIDOC
## getGoSize

### Description
Retrieves the number of genes associated with GO terms from an organism annotation database.

### Parameters
#### Request Body
- **terms** (character vector) - Required - GO IDs to query.
- **orgdb** (object) - Required - Organism annotation database.
- **keytype** (character) - Required - Key type for the database.
- **children** (logical) - Optional - Whether to include genes from child terms.
```

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

### calculateSimMatrix

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/index.html

Calculates the score similarity matrix between GO terms.

```APIDOC
## calculateSimMatrix

### Description
Calculate the score similarity matrix between terms.

### Method
Function Call

### Endpoint
calculateSimMatrix()
```

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

### View Clustering Results

Source: https://context7.com/ssayols/rrvgo/llms.txt

Displays the first few rows of the reducedTerms data frame, showing key columns like term, parentTerm, score, and termDispensability. Useful for a quick inspection of the clustering output.

```R
head(reducedTerms[, c("term", "parentTerm", "score", "termDispensability")])
```

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

### scatterPlot Function

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/scatterPlot.html

Visualizes GO terms as scattered points using dimensionality reduction.

```APIDOC
## scatterPlot

### Description
Plots GO terms as scattered points where distances represent similarity between terms. Axes are derived from dimensionality reduction algorithms (PCA or UMAP).

### Parameters
- **simMatrix** (matrix) - Required - A square similarity matrix.
- **reducedTerms** (data.frame) - Required - Data frame with reduced terms from reduceSimMatrix().
- **algorithm** (string) - Optional - Algorithm for dimensionality reduction: 'pca' or 'umap'.
- **onlyParents** (boolean) - Optional - If TRUE, plots only parent terms.
- **size** (string) - Optional - Metric for point size: 'size' or 'score'.
- **addLabel** (boolean) - Optional - Whether to add labels for the most representative term.
- **labelSize** (numeric) - Optional - Text size for labels.

### Response
- **ggplot2 object** - A ggplot2 object ready to be printed or manipulated.
```

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

### Create Treemap Plot with Custom Title

Source: https://context7.com/ssayols/rrvgo/llms.txt

Generates a treemap plot with a user-defined title to describe the GO enrichment results.

```r
treemapPlot(
  reducedTerms = reducedTerms,
  title = "GO Biological Process Enrichment"
)
```

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

### reduceSimMatrix

Source: https://context7.com/ssayols/rrvgo/llms.txt

Reduces a set of GO terms by grouping semantically similar terms and selecting a representative parent term for each cluster.

```APIDOC
## reduceSimMatrix

### Description
Reduces GO terms by performing hierarchical clustering on the similarity matrix and cutting the tree at a specified threshold.

### Parameters
#### Arguments
- **simMatrix** (matrix) - Required - The similarity matrix generated by calculateSimMatrix.
- **scores** (vector/string) - Required - Named vector of scores (names are GO IDs) or a string ("uniqueness" or "size") to determine representative terms.
- **threshold** (numeric) - Required - Similarity threshold for clustering (e.g., 0.4 to 0.9).
- **orgdb** (string) - Required - Organism annotation database.

### Response
- **reducedTerms** (data.frame) - A data frame containing clustering information including go, cluster, parent, score, size, term, parentTerm, and various uniqueness metrics.
```

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

### Reduce GO Term Redundancy in R

Source: https://context7.com/ssayols/rrvgo/llms.txt

Groups semantically similar GO terms using hierarchical clustering and selects representative parent terms. Scores can be provided as custom vectors or predefined metrics like 'uniqueness' or 'size'.

```r
library(rrvgo)

# Load data and calculate similarity matrix
go_analysis <- read.delim(system.file("extdata/example.txt", package="rrvgo"))
simMatrix <- calculateSimMatrix(go_analysis$ID, orgdb="org.Hs.eg.db", ont="BP", method="Rel")

# Create scores vector from p-values (higher = better, so use -log10 transformation)
scores <- setNames(-log10(go_analysis$qvalue), go_analysis$ID)

# Reduce terms using custom scores
# Threshold guidance: Large (0.9), Medium (0.7), Small (0.5), Tiny (0.4)
# Higher threshold = fewer groups, lower threshold = more groups
reducedTerms <- reduceSimMatrix(
  simMatrix = simMatrix,
  scores = scores,              # Named vector: names are GO IDs, values are scores
  threshold = 0.7,              # Similarity threshold for clustering
  orgdb = "org.Hs.eg.db"
)

# Alternative: Use term uniqueness as score (default if no scores provided)
reducedTerms_uniq <- reduceSimMatrix(
  simMatrix = simMatrix,
  scores = "uniqueness",        # Score by how unique each term is
  threshold = 0.7,
  orgdb = "org.Hs.eg.db"
)

# Alternative: Use GO term size as score (favors broader terms)
reducedTerms_size <- reduceSimMatrix(
  simMatrix = simMatrix,
  scores = "size",              # Score by number of genes in term
  threshold = 0.7,
  orgdb = "org.Hs.eg.db"
)

# Result: data.frame with clustering information
# Columns: go, cluster, parent, score, size, term, parentTerm,
#          termUniqueness, termUniquenessWithinCluster, termDispensability
head(reducedTerms)
#           go cluster     parent    score size                            term
# 1 GO:0006119       1 GO:0006119 4.567890  150 oxidative phosphorylation
```

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

### heatmapPlot Function

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/heatmapPlot.html

Visualizes a similarity matrix as a heatmap, optionally including reduced terms and parent annotations.

```APIDOC
## heatmapPlot

### Description
Plots a similarity matrix as a heatmap. This function is designed to visualize the similarity scores between GO terms.

### Parameters
- **simMatrix** (matrix) - Required - A square similarity matrix.
- **reducedTerms** (data.frame) - Optional - Data frame containing reduced terms from reduceSimMatrix().
- **annotateParent** (boolean) - Optional - Whether to add annotation of the parent term. Defaults to TRUE.
- **annotationLabel** (string) - Optional - Display "parent" ids or "parentTerm" string. Defaults to "parentTerm".
- **...** (dots) - Optional - Other parameters passed to pheatmap::pheatmap().

### Value
- **pheatmap object** (list) - Invisibly returns a pheatmap object.

### Request Example
heatmapPlot(simMatrix, reducedTerms, annotateParent=TRUE, annotationLabel="parentTerm", fontsize=6)
```

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

### reduceSimMatrix Function

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/reduceSimMatrix.html

Reduces a set of GO terms based on their semantic similarity and scores by clustering them and selecting a representative term for each group.

```APIDOC
## reduceSimMatrix

### Description
Reduces a set of GO terms based on their semantic similarity and scores. It groups terms that are within a similarity threshold and selects a representative term based on a score.

### Parameters
- **simMatrix** (matrix) - Required - A square similarity matrix.
- **scores** (vector) - Optional - One of c("uniqueness", "size"), or a named vector with scores for each term. Defaults to "uniqueness".
- **threshold** (numeric) - Optional - Similarity threshold (0-1). Defaults to 0.7.
- **orgdb** (object/string) - Required - An org.* Bioconductor package or orgdb object.
- **keytype** (string) - Optional - Keytype passed to AnnotationDbi::keys. Defaults to "ENTREZID".
- **children** (boolean) - Optional - Whether to include genes in children terms when retrieving GO term size. Defaults to TRUE.

### Response
- **data.frame** - A data frame identifying clusters of terms, the parent representative term, and metrics of importance (uniqueness and dispensability).

### Request Example
reduceSimMatrix(simMatrix, scores = c("uniqueness"), threshold = 0.7, orgdb = "org.Hs.eg.db")
```

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

### Export Reduced GO Terms

Source: https://context7.com/ssayols/rrvgo/llms.txt

Writes the reduced GO terms data frame to a CSV file. Ensures row names are not included in the output.

```R
write.csv(reducedTerms, "reduced_go_terms.csv", row.names=FALSE)
```

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

### Create Scatter Plot Showing Only Parents

Source: https://context7.com/ssayols/rrvgo/llms.txt

Generates a scatter plot displaying only parent GO terms (cluster representatives). Point size reflects the number of terms within each cluster.

```r
scatterPlot(
  simMatrix = simMatrix,
  reducedTerms = reducedTerms,
  onlyParents = TRUE            # Only show cluster representatives
)
```

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

### Export Parent GO Terms

Source: https://context7.com/ssayols/rrvgo/llms.txt

Filters and exports only the parent GO terms (cluster representatives) to a CSV file. This isolates the representative terms from the clustering results.

```R
parents_only <- reducedTerms[reducedTerms$go == reducedTerms$parent, ]
write.csv(parents_only, "parent_go_terms.csv", row.names=FALSE)
```

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

### Customize Heatmap Font Sizes

Source: https://context7.com/ssayols/rrvgo/llms.txt

Generates a heatmap with customized font sizes for the main plot and row/column labels, useful for heatmaps with many terms.

```r
heatmapPlot(
  simMatrix = simMatrix,
  reducedTerms = reducedTerms,
  annotateParent = TRUE,
  annotationLabel = "parentTerm",
  fontsize = 6,                 # Smaller font for many terms
  fontsize_row = 5,
  fontsize_col = 5
)
```

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

### Create Treemap Plot Sized by Term Size

Source: https://context7.com/ssayols/rrvgo/llms.txt

Generates a treemap plot where the size of the rectangles is determined by the GO term size rather than the score.

```r
treemapPlot(
  reducedTerms = reducedTerms,
  size = "size"                 # "score" (default) or "size"
)
```

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

### wordcloudPlot Function

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/wordcloudPlot.html

Plots Gene Ontology (GO) reduced terms as a word cloud. This function is useful for visualizing the most frequent GO terms after similarity reduction.

```APIDOC
## wordcloudPlot

### Description
Plot GO reduced terms as a wordcloud.

### Method
`wordcloudPlot(reducedTerms, onlyParents = TRUE, ...)`

### Parameters
#### Arguments
- **reducedTerms** (data.frame) - A data frame with the reduced terms from `reduceSimMatrix()`.
- **onlyParents** (logical) - Use only parent terms to calculate frequencies. Defaults to TRUE.
- **...** - Other parameters sent to `wordcloud::wordcloud()`.

### Value
Nothing

### Examples
```R
go_analysis <- read.delim(system.file("extdata/example.txt", package="rrvgo"))
simMatrix <- calculateSimMatrix(go_analysis$ID, orgdb="org.Hs.eg.db", ont="BP", method="Rel")
scores <- setNames(-log10(go_analysis$qvalue), go_analysis$ID)
reducedTerms <- reduceSimMatrix(simMatrix, scores, threshold=0.7, orgdb="org.Hs.eg.db")
wordcloudPlot(reducedTerms, min.freq=1, colors="black")
```
```

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

### treemapPlot Function

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/treemapPlot.html

Plots Gene Ontology (GO) terms as a treemap using the treemapPlot function.

```APIDOC
## treemapPlot

### Description
Plot GO terms as a treemap.

### Method
`treemapPlot(reducedTerms, size = "score", title = "", ...)`

### Parameters
#### Arguments
- **reducedTerms** (data.frame) - a data.frame with the reduced terms from reduceSimMatrix()
- **size** (string) - what to use as point size. Can be either GO term's "size" or "score". Defaults to "score".
- **title** (string) - title of the plot. Defaults to nothing.
- **...** - other parameters sent to treemap::treemap()

### Value
A list from the call to the `treemap()` function is silently returned.

### Examples
```R
if (FALSE) { # \dontrun{
go_analysis <- read.delim(system.file("extdata/example.txt", package="rrvgo"))
simMatrix <- calculateSimMatrix(go_analysis$ID, orgdb="org.Hs.eg.db", ont="BP", method="Rel")
scores <- setNames(-log10(go_analysis$qvalue), go_analysis$ID)
reducedTerms <- reduceSimMatrix(simMatrix, scores, threshold=0.7, orgdb="org.Hs.eg.db")
treemapPlot(reducedTerms)
}
```
```

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

### Customize Scatter Plot with ggplot2

Source: https://context7.com/ssayols/rrvgo/llms.txt

Generates a scatter plot and returns a ggplot2 object, allowing for further customization such as adding a custom title.

```r
p <- scatterPlot(simMatrix, reducedTerms)
p + ggplot2::ggtitle("Reduced GO Terms - Biological Process")
```

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

### Retrieve GO Term Gene Counts

Source: https://context7.com/ssayols/rrvgo/llms.txt

Calculates the number of genes associated with GO terms, with options to include or exclude child term annotations.

```r
library(rrvgo)

# Get GO term sizes (number of genes)
go_ids <- c("GO:0006119", "GO:0042775", "GO:0022904")

# Include genes from child terms (default)
sizes <- getGoSize(
  terms = go_ids,
  orgdb = "org.Hs.eg.db",
  keytype = "ENTREZID",
  children = TRUE               # Include genes in child terms
)

print(sizes)

# Count only direct annotations (no child terms)
sizes_direct <- getGoSize(
  terms = go_ids,
  orgdb = "org.Hs.eg.db",
  keytype = "ENTREZID",
  children = FALSE
)
```

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

### Customize Treemap Labels and Fonts

Source: https://context7.com/ssayols/rrvgo/llms.txt

Generates a treemap plot with custom font sizes and styles for parent and child labels, allowing for better readability.

```r
treemapPlot(
  reducedTerms = reducedTerms,
  title = "GO Terms",
  fontsize.labels = c(14, 10), # Font sizes for parent and child labels
  fontface.labels = c("bold", "plain")
)
```

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

### Save Treemap Plot to File

Source: https://context7.com/ssayols/rrvgo/llms.txt

Saves the generated treemap plot to a PNG file with specified dimensions and title.

```r
png("go_treemap.png", width=800, height=600)
treemapPlot(reducedTerms, title="Enriched GO Terms")
dev.off()
```

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

### Create Scatter Plot Without Labels

Source: https://context7.com/ssayols/rrvgo/llms.txt

Generates a scatter plot without labels for parent terms, providing a cleaner visualization.

```r
scatterPlot(
  simMatrix = simMatrix,
  reducedTerms = reducedTerms,
  addLabel = FALSE
)
```

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

### getTermDisp Function

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/getTermDisp.html

Calculates the term dispensability score based on a similarity matrix and cluster assignments.

```APIDOC
## getTermDisp

### Description
Calculate the term dispensability score, defined as the semantic similarity threshold a term was assigned to a cluster (namely, the similarity of a term to the cluster representative term).

### Parameters
- **simMatrix** (matrix) - Required - A square similarity matrix.
- **cluster** (vector) - Required - The cluster assignment for each term.
- **clusterRep** (vector) - Required - The cluster representative term.

### Value
A vector of term dispensability scores.
```

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

### Customize Scatter Plot Appearance

Source: https://context7.com/ssayols/rrvgo/llms.txt

Customizes the scatter plot by sizing points based on GO term size and adding labels for parent terms with a specified font size.

```r
scatterPlot(
  simMatrix = simMatrix,
  reducedTerms = reducedTerms,
  size = "size",                # Size points by GO term size instead of score
  addLabel = TRUE,              # Add labels for parent terms
  labelSize = 4                 # Font size for labels
)
```

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

### getTermUniq Function

Source: https://github.com/ssayols/rrvgo/blob/master/docs/reference/getTermUniq.html

Calculate the term uniqueness score, defined as 1 minus the average semantic similarity of a term to all other terms.

```APIDOC
## getTermUniq

### Description
Calculate the term uniqueness score, defined as 1 minus the average semantic similarity of a term to all other terms.

### Parameters
#### Arguments
- **simMatrix** (matrix) - Required - A square similarity matrix.
- **cluster** (vector) - Optional - Vector with the cluster each entry in the simMatrix belongs to. If NULL, a default behavior is applied.

### Response
- **Returns** (vector) - A vector of term uniqueness scores.
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.