ReactomePA

https://github.com/yulab-smu/reactomepa
Admin
ReactomePA is an R/Bioconductor package that provides pathway analysis functions including...
Tokens:29,128
Snippets:337
Trust Score:8.5
Update:3 months ago
Show doc for...
Tokens:
Raw
### Install Organism-Specific Annotation Packages

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/configuration.md

Required installation commands for specific organism annotation databases.

```r
# For human
BiocManager::install("org.Hs.eg.db")

# For mouse
BiocManager::install("org.Mm.eg.db")

# For fly
BiocManager::install("org.Dm.eg.db")
```

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

### Install ReactomePA

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

Use BiocManager to install the package from Bioconductor.

```r
# Install from Bioconductor
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("ReactomePA")

library(ReactomePA)
```

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

### Construct networks for different organisms

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/prepareReactomeNetwork.md

Examples of generating networks for various supported model organisms.

```r
# Mouse
net_mouse <- prepareReactomeNetwork("mouse")

# Fly
net_fly <- prepareReactomeNetwork("fly")

# Yeast
net_yeast <- prepareReactomeNetwork("yeast")
```

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

### Retrieve Annotation Database Names

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/configuration.md

Examples of using the getDb function to resolve the OrgDb package name for specific organisms.

```r
# Function: getDb(organism)
# Returns the OrgDb package name
# Examples:
getDb("human")      # "org.Hs.eg.db"
getDb("mouse")      # "org.Mm.eg.db"
getDb("fly")        # "org.Dm.eg.db"
```

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

### Perform basic pathway enrichment analysis in R

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/START_HERE.md

Installs the package via BiocManager, performs enrichment analysis on a gene list, and generates a dotplot visualization.

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

# Load
library(ReactomePA)

# Analyze
genes <- c("1017", "249", "5170", "11171", "8243")
result <- enrichPathway(genes, pvalueCutoff = 0.05)

# View
head(as.data.frame(result))

# Plot
dotplot(result)
```

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

### Dot Plot Visualization

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/visualization-functions.md

Defines the dotplot function signature and provides a usage example for visualizing enrichment results.

```r
dotplot(object, x = "GeneRatio", color = "p.adjust", size = "Count",
        showCategory = 30, split = NULL, font.size = 12, title = "",
        orderBy = "x", decreasing = FALSE, displayNum = NULL, ...)
```

```r
result <- enrichPathway(gene, pvalueCutoff = 0.05)
dotplot(result, showCategory = 20)
```

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

### Gene-Pathway Heatmap

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/visualization-functions.md

Defines the heatplot function signature and provides an example for visualizing gene-pathway associations.

```r
heatplot(object, showCategory = 30, colorBy = "p.adjust",
         label_format = 30, high = "red", low = "blue",
         breaks = "n", foldChange = NULL, ...)
```

```r
result <- enrichPathway(gene, pvalueCutoff = 0.05)
heatplot(result, showCategory = 15)
```

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

### Inspecting Reactome network sparse matrices

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/types.md

Shows how to prepare a Reactome network and inspect the dimensions and sparsity of the resulting dgCMatrix.

```r
net <- prepareReactomeNetwork("human")

# Inspect structure
dim(net)              # e.g., 20000 x 20000
net[1:5, 1:5]         # Preview
sum(net != 0)         # Number of edges

# Use in RWR
# (Internal to nsePathway, but can be used directly)
```

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

### View Directory Structure

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/INDEX.md

Displays the file organization of the ReactomePA documentation project.

```text
output/
├── INDEX.md                          # This file
├── README.md                         # Main entry point with overview
├── api-summary.md                    # Quick reference of all exports
├── types.md                          # Type definitions and data structures
├── configuration.md                  # Setup, parameters, tuning
├── internals.md                      # Internal functions and dependencies
└── api-reference/                    # Detailed function documentation
    ├── enrichPathway.md              # Over-representation analysis
    ├── gsePathway.md                 # Gene set enrichment analysis
    ├── nsePathway.md                 # Network-based enrichment
    ├── bayes_reactome.md             # Bayesian filtering
    ├── gson_Reactome.md              # Build GSON annotation objects
    ├── prepareReactomeNetwork.md     # Build reaction networks
    ├── viewPathway.md                # Pathway topology visualization
    └── visualization-functions.md    # All plotting functions
```

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

### GSEA Ridge Plot

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/visualization-functions.md

Defines the ridgeplot function signature and provides an example for visualizing GSEA results.

```r
ridgeplot(object, showCategory = 30, fill = "p.adjust",
         core_enrichment = TRUE, ...)
```

```r
result <- gsePathway(geneList, pvalueCutoff = 0.05)
ridgeplot(result, showCategory = 20)
```

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

### Basic network construction

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/prepareReactomeNetwork.md

Initializes a Reactome network for a specified organism and provides basic inspection methods.

```r
library(ReactomePA)

# Build network for human (will be cached)
net <- prepareReactomeNetwork("human")

# Inspect the network
dim(net)                # dimensions
sum(net != 0)           # number of edges
Net[1:5, 1:5]           # preview
```

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

### Enrichment Map Plot

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/visualization-functions.md

Defines the emapplot function signature and provides an example for visualizing pathway relationships.

```r
emapplot(object, showCategory = 30, pie = "equal", layout = "kk",
         repel = FALSE, cex_label_category = 1, ...)
```

```r
result <- enrichPathway(gene, pvalueCutoff = 0.05)
emapplot(result, showCategory = 20, layout = "fr")
```

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

### Gene-Concept Network Plot

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/visualization-functions.md

Defines the cnetplot function signature and provides an example showing relationships between genes and pathways.

```r
cnetplot(object, showCategory = 5, categorySize = "pvalue",
         color.params = list(foldChange = NULL, edge = TRUE),
         layout = "kk", vertex.label.font = 1, ...)
```

```r
result <- enrichPathway(gene, pvalueCutoff = 0.05)
result@foldChange <- c("1017" = 2.1, "249" = -1.5)
cnetplot(result, showCategory = 5)
```

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

### Check available organisms

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/gson_Reactome.md

Demonstrates how to build a GSON object for a specific organism like mouse.

```r
# Supported organisms (from NAMESPACE)
organisms <- c("human", "rat", "mouse", "celegans", "yeast", "zebrafish", "fly")

# Build for mouse
gson_mouse <- gson_Reactome("mouse")
```

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

### Optimize Performance via Caching

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/configuration.md

Demonstrates caching behavior for pathway enrichment and network preparation to speed up subsequent function calls.

```r
# First call (builds and caches)
result1 <- enrichPathway(gene1, organism = "human")  # ~5-10 seconds

# Subsequent calls (use cache)
result2 <- enrichPathway(gene2, organism = "human")  # ~1-2 seconds
result3 <- enrichPathway(gene3, organism = "human")  # ~1-2 seconds

# For NSEA with network reuse
network <- prepareReactomeNetwork("human")  # One-time: ~30-60 seconds
result1 <- nsePathway(genes1, network = network)  # Fast
result2 <- nsePathway(genes2, network = network)  # Fast
```

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

### Reuse network for nsePathway

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/prepareReactomeNetwork.md

Demonstrates building a network once and reusing it across multiple nsePathway analyses to improve performance.

```r
# Build once, reuse multiple times
network <- prepareReactomeNetwork("human")

# Run multiple NSEA analyses
geneList1 <- c("1017" = 2.5, "249" = -1.8)
geneList2 <- c("5170" = 3.2, "11171" = -2.1)

result1 <- nsePathway(geneList1, network = network)
result2 <- nsePathway(geneList2, network = network)
```

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

### Use a pre-built network for repeated analyses

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/nsePathway.md

Builds the Reactome network once to optimize performance when running multiple analyses.

```r
# Build network once for repeated analyses
network <- prepareReactomeNetwork("human")

# Run multiple NSEA analyses with same network
result1 <- nsePathway(geneList1, network = network, pvalueCutoff = 0.05)
result2 <- nsePathway(geneList2, network = network, pvalueCutoff = 0.05)
```

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

### Update Package Documentation

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/internals.md

Run this command to regenerate the NAMESPACE and man/ files after modifying roxygen2 comments in the source code.

```r
roxygen2::roxygenise()
```

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

### prepareReactomeNetwork(organism = "human", directed = FALSE)

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/prepareReactomeNetwork.md

Builds and caches the Reactome reaction network as a sparse matrix for use with network-based set enrichment analysis (NSEA).

```APIDOC
## prepareReactomeNetwork

### Description
Builds and caches the Reactome reaction network as a sparse matrix for use with network-based set enrichment analysis (NSEA). The network represents gene-gene interactions based on co-participation in Reactome reaction steps.

### Signature
`prepareReactomeNetwork(organism = "human", directed = FALSE)`

### Parameters
- **organism** (character) - Optional - Supported organism: "human", "mouse", "rat", "yeast", "zebrafish", "fly", "celegans", or others supported by graphite package. Default: "human".
- **directed** (logical) - Optional - If FALSE (default), edges are undirected (reciprocal edges added). If TRUE, edges retain direction from pathway graphs. Default: FALSE.

### Return Value
Returns a sparse matrix (dgCMatrix from Matrix package):
- Rows: genes (Entrez IDs as row names)
- Columns: genes (Entrez IDs as column names)
- Values: column-normalized edge weights (0-1 per column sums to 1)

### Errors
- Raises an error if the organism is not supported.
- Raises an error if no Reactome pathways are found for the organism.
- Raises an error if pathway conversions fail or no usable edges are extracted.
```

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

### Accessing GSON object data

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/types.md

Demonstrates how to initialize a GSON object for human data and access its internal mappings and metadata.

```r
gson <- gson_Reactome("human")

# Access pathway-to-gene mapping
head(gson@gsid2gene)

# Access gene-to-pathway mapping
head(gson@gsid2name)

# Check version
cat(gson@version)
cat(gson@info)
```

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

### Basic usage

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/gson_Reactome.md

Builds a GSON object for human data and inspects the internal data frames.

```r
library(ReactomePA)

# Build GSON for human
gson <- gson_Reactome("human")

# Inspect the GSON object
head(gson@gsid2gene)
head(gson@gsid2name)
```

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

### List supported organisms

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-summary.md

Supported organisms for ReactomePA analysis. Each requires the corresponding OrgDb package.

```r
c("human", "rat", "mouse", "celegans", "yeast", "zebrafish", "fly",
  "arabidopsis", "bovine", "canine", "chicken", "ecolik12",
  "ecsakai", "gondii", "malaria", "pig", "rhesus", "xenopus")
```

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

### Complete ORA Workflow

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

Run a standard over-representation analysis and generate summary plots and pathway views.

```r
library(ReactomePA)

genes <- c("1017", "249", "5170", "11171")
result <- enrichPathway(genes, readable = TRUE)
summary(result)

dotplot(result)
emapplot(result)
viewPathway("Cell Cycle", readable = TRUE)
```

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

### Perform NSEA with Network Reuse

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-summary.md

Optimizes performance by preparing the Reactome network once for multiple analysis runs.

```r
# 1. Build network once
network <- prepareReactomeNetwork("human")

# 2. Run multiple analyses
result1 <- nsePathway(geneList1, network = network)
result2 <- nsePathway(geneList2, network = network)

# 3. Compare results
dotplot(result1)
dotplot(result2)
```

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

### prepareReactomeNetwork(organism, directed = FALSE)

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/prepareReactomeNetwork.md

Constructs a sparse matrix network from Reactome pathways for a given organism. The function supports caching to improve performance on subsequent calls within the same R session.

```APIDOC
## prepareReactomeNetwork(organism, directed = FALSE)

### Description
Constructs a sparse matrix representing the Reactome pathway network for a specified organism. The resulting matrix is column-normalized and suitable for use in network-based analysis methods like `nsePathway`.

### Parameters
- **organism** (string) - Required - The name of the organism (e.g., "human", "mouse", "fly"). Must be supported by the `graphite` package.
- **directed** (boolean) - Optional - Whether to preserve reaction directionality. Defaults to FALSE (undirected).

### Returns
- **matrix** (sparse matrix) - A column-normalized sparse matrix where entry (i,j) represents the normalized weight of the edge from gene j to gene i.

### Example
```r
# Build a directed network for human
net <- prepareReactomeNetwork("human", directed = TRUE)

# Use in analysis
result <- nsePathway(geneList, network = net)
```
```

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

### Access version information

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/gson_Reactome.md

Retrieves and displays metadata regarding the Reactome database version and source information.

```r
gson <- gson_Reactome("human")

# Get version info
cat(gson@version)
cat(gson@info)
```

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

### viewPathway(pathName, organism, readable, foldChange, keyType, layout)

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/viewPathway.md

Visualize a specific Reactome pathway as a network graph with optional fold-change coloring.

```APIDOC
## viewPathway

### Description
Visualize a specific Reactome pathway as a network graph with optional fold-change coloring. Creates an interactive ggraph-based plot showing gene products and their interactions within the pathway.

### Function Signature
viewPathway(pathName, organism = "human", readable = TRUE, foldChange = NULL, keyType = "ENTREZID", layout = "kk")

### Parameters
- **pathName** (character) - Required - Name of the Reactome pathway to visualize. Must be an exact pathway name from graphite.
- **organism** (character) - Optional - Supported organism: "human", "rat", "mouse", "celegans", "yeast", "zebrafish", "fly". Default: "human".
- **readable** (logical) - Optional - If TRUE, converts node IDs to gene symbols; if FALSE, uses Entrez IDs. Default: TRUE.
- **foldChange** (numeric vector) - Optional - Named numeric vector of fold-changes for coloring nodes. Names should match keyType. Default: NULL.
- **keyType** (character) - Optional - Type of gene identifier in names of foldChange vector. Default: "ENTREZID".
- **layout** (character) - Optional - Graph layout algorithm: "kk" (Kamada-Kawai, default), "fr" (Fruchterman-Reingold), "circle", "linear". Default: "kk".

### Return Value
Returns a ggplot object (ggraph-based) showing nodes (gene products), edges (interactions), colors (based on foldChange or default gray), and labels (gene names or Entrez IDs).
```

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

### Perform Basic ORA and Visualization

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/START_HERE.md

Executes pathway enrichment analysis and generates standard plots for visualization.

```r
result <- enrichPathway(genes, pvalueCutoff = 0.05)
dotplot(result)
cnetplot(result)
```

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

### Visualize a basic pathway in R

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/viewPathway.md

Generates a network plot for a specified Reactome pathway name.

```r
library(ReactomePA)

# View a pathway as network (requires pathway name)
plot <- viewPathway("Cell Cycle", organism = "human")
print(plot)
```

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

### Configure RWR parameters

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/nsePathway.md

Adjusts Random Walk with Restart (RWR) parameters to control propagation depth and convergence.

```r
# Lower restart probability for deeper propagation
result <- nsePathway(
  geneList,
  restart_prob = 0.3,
  threshold = 1e-8,
  maxIter = 150,
  pvalueCutoff = 0.05
)
```

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

### Perform Network-Based Analysis

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/START_HERE.md

Prepares a Reactome network for a specific organism and performs pathway analysis based on that network.

```r
net <- prepareReactomeNetwork("human")
result <- nsePathway(geneList, network = net)
dotplot(result)
```

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

### Map organism names with getDb

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/internals.md

Maps organism names to their corresponding OrgDb package names. Handles legacy 'worm' to 'celegans' conversion.

```r
getDb(organism)
```

```r
getDb <- function(organism) {
    if (organism == "worm") {
        organism = "celegans"
        warning("'worm' is deprecated, please use 'celegans' instead...")
    }
    
    annoDb <- switch(organism,
                     anopheles   = "org.Ag.eg.db",
                     arabidopsis = "org.At.tair.db",
                     bovine      = "org.Bt.eg.db",
                     # ... (20+ organism mappings)
                     zebrafish   = "org.Dr.eg.db",
                     )
    return(annoDb)
}
```

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

### Generate Standard Enrichment Plots

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/visualization-functions.md

Demonstrates the workflow for generating dot plots, network plots, enrichment maps, and heatmaps for enrichment results.

```r
result <- enrichPathway(gene, pvalueCutoff = 0.05)

# Dot plot
p1 <- dotplot(result, showCategory = 20)

# Gene-concept network
p2 <- cnetplot(result, showCategory = 10)

# Enrichment map
p3 <- emapplot(result, showCategory = 20)

# Gene-pathway heatmap
p4 <- heatplot(result, showCategory = 15)

# Combine plots
# (requires gridExtra or patchwork)
gridExtra::grid.arrange(p1, p2, p3, p4, ncol = 2)
```

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

### Define viewPathway layout values

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-summary.md

Available igraph layout algorithms for network visualization.

```r
c("kk", "fr", "circle", "linear")
```

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

### viewPathway(pathName, organism, readable, foldChange, keyType, layout)

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/viewPathway.md

Visualizes a specified Reactome pathway as a network plot. Users can customize the visualization with fold-change data, gene ID types, and different network layout algorithms.

```APIDOC
## viewPathway(pathName, organism = "human", readable = FALSE, foldChange = NULL, keyType = "ENTREZID", layout = "kk")

### Description
Visualizes a Reactome pathway as a network. The function generates a plot object that can be rendered to display pathway components and their interactions.

### Parameters
- **pathName** (string) - Required - The exact name of the Reactome pathway to visualize.
- **organism** (string) - Optional - The organism name (e.g., "human", "mouse", "fly"). Defaults to "human".
- **readable** (boolean) - Optional - If TRUE, converts gene IDs to readable symbols. Defaults to FALSE.
- **foldChange** (numeric vector) - Optional - A named vector of fold-change values for genes to color the network nodes.
- **keyType** (string) - Optional - The type of gene ID used in foldChange (e.g., "SYMBOL", "ENTREZID"). Defaults to "ENTREZID".
- **layout** (string) - Optional - The layout algorithm for the network plot. Options include "kk" (Kamada-Kawai), "fr" (Fruchterman-Reingold), and "circle". Defaults to "kk".

### Errors
- Raises an error if pathName does not exist.
- Raises an error if the organism is not supported.
- Raises an error if foldChange contains duplicate gene names.
- Raises an error if the pathway cannot be converted to the requested ID type.
```

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

### Access gseaResult data

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/types.md

Demonstrates how to run GSEA and access specific enrichment metrics like NES and core enrichment genes.

```r
result <- gsePathway(geneList, pvalueCutoff = 0.05)

# GSEA-specific operations
head(result@result)
result@result$NES        # Normalized enrichment scores
result@result$core_enrichment  # Driver genes
```

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

### Configure MCMC sampling parameters

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/bayes_reactome.md

Increases MCMC iterations and thinning for more stable estimates when analyzing large candidate sets.

```r
# For more stable estimates with larger candidate sets
bx <- bayes_reactome(
  x,
  candidate = "all",
  n_terms = Inf,            # No limit
  n_iter = 10000,
  burnin = 2000,
  thin = 2,                 # Keep every 2nd sample
  verbose = TRUE
)
```

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

### List Available Adjustment Methods

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/configuration.md

Displays the supported multiple testing correction methods for enrichment analysis.

```r
c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none")
```

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

### Access nseaResult data

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/types.md

Demonstrates how to run network-based enrichment and access the underlying sparse network matrix.

```r
result <- nsePathway(geneList)

# Results similar to gseaResult, but with network-diffused scores
head(result@result)

# Access network
result@network  # dgCMatrix with gene interactions
```

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

### Apply different network layout algorithms

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/viewPathway.md

Adjusts the visual arrangement of the pathway network using supported layout parameters.

```r
# Fruchterman-Reingold layout (better for complex networks)
plot_fr <- viewPathway(
  "Apoptosis",
  layout = "fr"
)

# Circular layout
plot_circ <- viewPathway(
  "Apoptosis",
  layout = "circle"
)

# Kamada-Kawai layout (default)
plot_kk <- viewPathway(
  "Apoptosis",
  layout = "kk"
)
```

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

### Configure Statistical Thresholds

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/configuration.md

Adjust p-value cutoffs and adjustment methods to balance sensitivity and specificity in pathway enrichment results.

```r
# Conservative (few false positives)
enrichPathway(gene, pvalueCutoff=0.01, pAdjustMethod="bonferroni")

# Standard (good balance)
enrichPathway(gene, pvalueCutoff=0.05, pAdjustMethod="BH")

# Liberal (explore more pathways, may have false positives)
enrichPathway(gene, pvalueCutoff=0.1, pAdjustMethod="BH")
```

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

### Perform Basic GSEA

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/gsePathway.md

Executes GSEA on a named numeric vector of log2 fold-changes.

```r
library(ReactomePA)

# Create a ranked gene list (log2 fold-change)
geneList <- c("5170" = 4.2, "4171" = 3.8, "11171" = -2.5, 
              "1017" = -3.1, "2194" = 1.5, "249" = -0.8)

# Run GSEA
result <- gsePathway(geneList, pvalueCutoff = 0.05)

# View results
head(as.data.frame(result))
```

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

### Define gsePathway method values

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-summary.md

Valid methods for gsePathway. 'multilevel' is the recommended default for efficiency.

```r
c("sample", "permute", "multilevel")
```

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

### Configure directed vs undirected networks

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/prepareReactomeNetwork.md

Toggles between directed and undirected graph structures, where directed networks preserve reaction directionality.

```r
# Undirected network (default)
net_undirected <- prepareReactomeNetwork("human", directed = FALSE)

# Directed network (preserves reaction directionality)
net_directed <- prepareReactomeNetwork("human", directed = TRUE)

# Directed networks typically have fewer edges
```

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

### viewPathway

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-summary.md

Provides topological pathway visualization.

```APIDOC
## viewPathway

### Description
Provides topological pathway visualization.

### Signature
viewPathway(pathName, organism = "human", readable = TRUE, foldChange = NULL, keyType = "ENTREZID", layout = "kk")

### Returns
ggplot object
```

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

### Perform Complete ORA Workflow

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-summary.md

Executes a standard enrichment analysis using Entrez IDs and provides visualization methods.

```r
# 1. Prepare data
genes <- c("1017", "249", "5170")

# 2. Run analysis
result <- enrichPathway(genes, pvalueCutoff = 0.05)

# 3. Inspect
summary(result)
as.data.frame(result)

# 4. Visualize
dotplot(result)
cnetplot(result)
```

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

### Configure Custom Permutations

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/gsePathway.md

Adjusts permutation settings for smaller sample sizes using the multilevel method.

```r
# More permutations for smaller sample size
result <- gsePathway(
  geneList,
  pvalueCutoff = 0.05,
  method = "multilevel",
  nPerm = 10000,
  maxPerm = 1e6
)
```

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

### Perform Network-based Set Enrichment Analysis with nsePathway

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-summary.md

Executes NSEA using the Reactome reaction graph. Returns an nseaResult object.

```r
nsePathway(geneList, organism = "human", mode = c("evidence", "signed"),
           network = NULL, restart_prob = 0.5, specific_weight = FALSE,
           minGSSize = 10, maxGSSize = 500, threshold = 1e-9,
           maxIter = 100, verbose = TRUE, ...)
```

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

### enrichPathway(gene, pvalueCutoff = 0.05, pAdjustMethod = "BH", universe = NULL, weight = NULL, minGSSize = 10, maxGSSize = 500, qvalueCutoff = 0.2, organism = "human", readable = FALSE)

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/enrichPathway.md

Performs Over-Representation Analysis (ORA) for Reactome pathways using a vector of input genes.

```APIDOC
## enrichPathway

### Description
Performs Over-Representation Analysis (ORA) for Reactome pathways. Given a vector of input genes, this function identifies significantly enriched Reactome pathways using hypergeometric testing with false discovery rate control.

### Parameters
- **gene** (character vector) - Required - Entrez gene IDs of the input gene set
- **pvalueCutoff** (numeric) - Optional - P-value significance cutoff for reporting pathways (Default: 0.05)
- **pAdjustMethod** (character) - Optional - Method for multiple testing correction: "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none" (Default: "BH")
- **universe** (character vector) - Optional - Background genes (universe) for hypergeometric test. If NULL, all annotated genes are used (Default: NULL)
- **weight** (numeric vector) - Optional - Named numeric vector of weights for genes in the universe. Enables weighted ORA (Default: NULL)
- **minGSSize** (integer) - Optional - Minimum number of genes in a pathway for it to be tested (Default: 10)
- **maxGSSize** (integer) - Optional - Maximum number of genes in a pathway for it to be tested (Default: 500)
- **qvalueCutoff** (numeric) - Optional - Q-value (q-adjusted p-value) cutoff for reporting results (Default: 0.2)
- **organism** (character) - Optional - Organism: "human", "rat", "mouse", "celegans", "yeast", "zebrafish", "fly" (Default: "human")
- **readable** (logical) - Optional - If TRUE, maps gene IDs to human-readable gene symbols in the result (Default: FALSE)

### Return Value
Returns an `enrichResult` object containing the enrichment results, organism name, ontology type, and keytype.
```

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

### Access metadata in R

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-summary.md

Retrieve organism, ontology, and keytype information stored in the result object.

```r
result@organism
result@ontology
result@keytype
```

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

### Ensure Reproducibility for MCMC Methods

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/configuration.md

Set a seed when using bayes_reactome to ensure consistent results across different runs.

```r
# Set seed for bayes_reactome
result <- enrichPathway(gene, pvalueCutoff = 0.05)
result_bayes <- bayes_reactome(result, seed = 12345)

# gsePathway has deterministic RNG for most methods
# but use seed in underlying enrichit if needed
```

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

### Perform Gene Set Enrichment Analysis with gsePathway

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-summary.md

Executes GSEA for Reactome pathways. Requires a ranked gene list and returns a gseaResult object.

```r
gsePathway(geneList, organism = "human", exponent = 1, weight = NULL,
           minGSSize = 10, maxGSSize = 500, pvalueCutoff = 0.05,
           pAdjustMethod = "BH", verbose = TRUE, nPerm = 1000,
           method = "multilevel", adaptive = FALSE, minPerm = 101,
           maxPerm = 1e5, pvalThreshold = 0.1)
```

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

### gseaplot(object, geneSetID, by = "all", title = "", color = "green", color.params = list(), ...)

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/visualization-functions.md

Creates a GSEA enrichment plot showing the running enrichment score, gene positions in the ranked list, and cumulative distribution.

```APIDOC
## gseaplot

### Description
Creates a GSEA enrichment plot showing the running enrichment score (RES), gene positions in the ranked list, and cumulative distribution.

### Parameters
- **object** (gseaResult) - Required - The GSEA result object.
- **geneSetID** (character vector) - Required - ID(s) of pathway(s) to plot.
- **by** (string) - Optional - How to aggregate multiple gene sets: "all", "runningScore", "position".
- **title** (string) - Optional - Plot title.
- **color** (string) - Optional - Color for the curve.
- **color.params** (list) - Optional - Additional color parameters.

### Example
```r
result <- gsePathway(geneList, pvalueCutoff = 0.05)
pathway_id <- result@result$ID[1]
gseaplot(result, geneSetID = pathway_id)
```
```

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

### nsePathway(geneList, organism, mode, network, restart_prob, specific_weight, minGSSize, maxGSSize, threshold, maxIter, verbose, ...)

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/nsePathway.md

Performs Network-based Set Enrichment Analysis (NSEA) for Reactome pathways using gene-level scores.

```APIDOC
## nsePathway

### Description
Performs GSEA on gene scores propagated across the Reactome reaction network using Random Walk with Restart (RWR). This approach captures pathway-level signals that may be diluted in individual gene-level statistics.

### Function Signature
```r
nsePathway(geneList, organism = "human", mode = c("evidence", "signed"), network = NULL, restart_prob = 0.5, specific_weight = FALSE, minGSSize = 10, maxGSSize = 500, threshold = 1e-9, maxIter = 100, verbose = TRUE, ...)
```

### Parameters
- **geneList** (numeric vector) - Required - Named numeric vector of gene-level scores (e.g., log2 fold-change). Names must be Entrez gene IDs. Should be sorted in descending order.
- **organism** (character) - Optional - Supported organism: "human", "mouse", "rat", "yeast", "zebrafish", "fly", "celegans". Default: "human".
- **mode** (character) - Optional - Propagation mode: "evidence" (single pass) or "signed" (separate positive/negative). Default: "evidence".
- **network** (dgCMatrix) - Optional - Pre-built network from prepareReactomeNetwork(). If NULL, network is auto-built and cached.
- **restart_prob** (numeric) - Optional - Restart probability for Random Walk with Restart (0 to 1). Default: 0.5.
- **specific_weight** (logical) - Optional - If TRUE, apply gene-specificity weighting (TF-IDF style) during propagation. Default: FALSE.
- **minGSSize** (integer) - Optional - Minimum number of genes in a pathway for testing. Default: 10.
- **maxGSSize** (integer) - Optional - Maximum number of genes in a pathway for testing. Default: 500.
- **threshold** (numeric) - Optional - Convergence threshold for RWR iterations. Default: 1e-9.
- **maxIter** (integer) - Optional - Maximum number of RWR iterations. Default: 100.
- **verbose** (logical) - Optional - If TRUE, print progress messages. Default: TRUE.
- **...** (additional arguments) - Optional - Other arguments passed to enrichit::gsea_gson() (e.g., nPerm, method, pvalueCutoff, pAdjustMethod).
```

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

### Perform GSEA and Visualization

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-summary.md

Runs GSEA on a ranked gene list and generates enrichment plots.

```r
# 1. Prepare ranked list
geneList <- c("5170" = 4.2, "4171" = 3.8, "11171" = -2.5)

# 2. Run analysis
result <- gsePathway(geneList, pvalueCutoff = 0.05)

# 3. Visualize
dotplot(result)
ridgeplot(result)
gseaplot(result, geneSetID = result@result$ID[1])
```

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

### prepareReactomeNetwork Function Signature

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/prepareReactomeNetwork.md

Defines the function signature for building the Reactome network with organism and directionality parameters.

```r
prepareReactomeNetwork(organism = "human", directed = FALSE)
```

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

### gson_Reactome(organism = "human")

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/gson_Reactome.md

Builds and caches a GSON gene-set annotation object from the Reactome pathway database for a specified organism.

```APIDOC
## gson_Reactome(organism = "human")

### Description
Builds and caches a GSON (Gene Set Object Notation) gene-set annotation object from the Reactome pathway database. This object is used for enrichment analysis and maps genes to pathways.

### Parameters
- **organism** (character) - Optional - The organism to retrieve data for. Supported values: "human", "rat", "mouse", "celegans", "yeast", "zebrafish", "fly". Default is "human".

### Return Value
Returns a GSON object (S4 class) containing:
- **gsid2gene**: data.frame with gsid and gene columns
- **gsid2name**: data.frame with gsid and name columns
- **version**: Reactome database version and source date
- **species**: Organism name

### Usage Example
```r
library(ReactomePA)

# Build GSON for human
gson <- gson_Reactome("human")

# Inspect the GSON object
head(gson@gsid2gene)
head(gson@gsid2name)
```
```

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

### Perform Over-representation Analysis with enrichPathway

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-summary.md

Executes ORA for Reactome pathways. Requires a gene list and returns an enrichResult object.

```r
enrichPathway(gene, pvalueCutoff = 0.05, pAdjustMethod = "BH",
              universe = NULL, weight = NULL, minGSSize = 10,
              maxGSSize = 500, qvalueCutoff = 0.2, organism = "human",
              readable = FALSE)
```

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

### Visualize Pathways with viewPathway

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-summary.md

Generates a ggplot object for topological pathway visualization.

```r
viewPathway(pathName, organism = "human", readable = TRUE,
            foldChange = NULL, keyType = "ENTREZID", layout = "kk")
```

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

### Update Reactome Database

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/configuration.md

Commands to check the current version of the Reactome database and update the underlying package.

```r
# Check current version
gson <- gson_Reactome("human")
cat(gson@version)

# Update package
BiocManager::install("reactome.db")

# Clear cache to use updated data
# (Next call to gson_Reactome will rebuild)
```

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

### viewPathway Function Signature

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/viewPathway.md

Defines the parameters for visualizing a Reactome pathway, including organism selection, identifier readability, and optional fold-change data.

```r
viewPathway(
  pathName,
  organism = "human",
  readable = TRUE,
  foldChange = NULL,
  keyType = "ENTREZID",
  layout = "kk"
)
```

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

### geneInCategory(x)

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/visualization-functions.md

Returns a list mapping each pathway to its constituent genes.

```APIDOC
## geneInCategory

### Description
Returns a list mapping each pathway to its constituent genes.

### Parameters
- **x** (enrichResult) - Required - The enrichment result object.

### Example
```r
result <- enrichPathway(gene, pvalueCutoff = 0.05)
pathway_genes <- geneInCategory(result)
```
```

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

### Reduce Redundancy with Bayesian Filtering

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/START_HERE.md

Filters enrichment results to reduce redundancy using Bayesian methods and extracts active pathways.

```r
result <- enrichPathway(genes, pvalueCutoff = 0.1)
result_bayes <- bayes_reactome(result)
active <- as.data.frame(result_bayes)[result_bayes@result$bayes_active, ]
```

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

### gsePathway(geneList, organism, exponent, weight, minGSSize, maxGSSize, pvalueCutoff, pAdjustMethod, verbose, nPerm, method, adaptive, minPerm, maxPerm, pvalThreshold)

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/gsePathway.md

Performs Gene Set Enrichment Analysis (GSEA) for Reactome pathways on a ranked gene list.

```APIDOC
## gsePathway

### Description
Performs Gene Set Enrichment Analysis (GSEA) for Reactome pathways on a sorted gene list. It identifies pathways with significant bias toward the top or bottom of the ranking without requiring an arbitrary cutoff.

### Signature
`gsePathway(geneList, organism = "human", exponent = 1, weight = NULL, minGSSize = 10, maxGSSize = 500, pvalueCutoff = 0.05, pAdjustMethod = "BH", verbose = TRUE, nPerm = 1000, method = "multilevel", adaptive = FALSE, minPerm = 101, maxPerm = 1e5, pvalThreshold = 0.1)`

### Parameters
- **geneList** (numeric vector) - Required - Ranked and named numeric vector (Entrez gene IDs). Should be sorted in descending order.
- **organism** (character) - Optional - Organism: "human", "rat", "mouse", "celegans", "yeast", "zebrafish", "fly". Default: "human".
- **exponent** (numeric) - Optional - Exponent (p) for weighting each step in the running sum. Default: 1.
- **weight** (numeric vector) - Optional - Named numeric vector of weights matching geneList names.
- **minGSSize** (integer) - Optional - Minimum number of genes in a pathway for testing. Default: 10.
- **maxGSSize** (integer) - Optional - Maximum number of genes in a pathway for testing. Default: 500.
- **pvalueCutoff** (numeric) - Optional - P-value cutoff for reporting significant pathways. Default: 0.05.
- **pAdjustMethod** (character) - Optional - Multiple testing correction method. Default: "BH".
- **verbose** (logical) - Optional - If TRUE, print progress messages. Default: TRUE.
- **nPerm** (integer) - Optional - Number of permutations for the "permute" method. Default: 1000.
- **method** (character) - Optional - Permutation method: "sample", "permute", or "multilevel". Default: "multilevel".
- **adaptive** (logical) - Optional - If TRUE, use adaptive permutation sampling. Default: FALSE.
- **minPerm** (integer) - Optional - Minimum number of permutations for "multilevel" method. Default: 101.
- **maxPerm** (integer) - Optional - Maximum number of permutations for "multilevel" method. Default: 100000.
- **pvalThreshold** (numeric) - Optional - P-value threshold for the "multilevel" method. Default: 0.1.
```

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

### heatplot

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/visualization-functions.md

Creates a gene-pathway heatmap showing which genes are in which pathways.

```APIDOC
## heatplot(object, showCategory = 30, colorBy = "p.adjust", label_format = 30, high = "red", low = "blue", breaks = "n", foldChange = NULL, ...)

### Description
Creates a gene-pathway heatmap with genes as rows and pathways as columns. Shows which genes are in which pathways, with optional fold-change coloring.

### Parameters
- **object** (enrichResult) - Required - The enrichment result object.
- **showCategory** (numeric) - Optional - Number of pathways to display (default 30).
- **colorBy** (string) - Optional - Column for cell color: "p.adjust", "pvalue", etc.
- **label_format** (numeric) - Optional - Character wrap width for labels.
- **high, low** (string) - Optional - Colors for high/low values.
- **breaks** (string) - Optional - Color breaks method.
- **foldChange** (numeric vector) - Optional - Named numeric vector for gene value overlay.

### Example
```r
result <- enrichPathway(gene, pvalueCutoff = 0.05)
heatplot(result, showCategory = 15)
```
```

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

### Pass custom GSEA parameters

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/nsePathway.md

Utilizes the ellipsis argument to pass additional parameters directly to the underlying GSEA implementation.

```r
# Pass additional GSEA parameters
result <- nsePathway(
  geneList,
  pvalueCutoff = 0.05,
  pAdjustMethod = "fdr",
  nPerm = 5000,
  method = "multilevel"
)
```

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

### Set graph attributes with setting.graph.attributes

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/internals.md

Configures default visual attributes for igraph objects used in pathway visualization.

```r
setting.graph.attributes(g, node.size = 8,
                         node.color = "#B3B3B3",
                         edge.width = 2,
                         edge.color = "#8DA0CB")
```

```r
setting.graph.attributes <- function(g, node.size=8,
                                     node.color="#B3B3B3",
                                     edge.width=2,
                                     edge.color="#8DA0CB") {
    V(g)$size <- node.size
    V(g)$color <- node.color
    V(g)$label <- V(g)$name
    
    E(g)$width <- edge.width
    E(g)$color <- edge.color
    
    return(g)
}
```

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

### Define nsePathway mode values

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-summary.md

Propagation modes for nsePathway. 'evidence' performs single propagation, while 'signed' separates positive and negative signals.

```r
c("evidence", "signed")
```

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

### Visualize pathways for non-human organisms

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/viewPathway.md

Specifies the organism parameter to retrieve pathways for species other than human.

```r
# Mouse pathways
plot <- viewPathway(
  "Cell Cycle",
  organism = "mouse",
  readable = TRUE
)

# Fly
plot <- viewPathway(
  "Signal Transduction",
  organism = "fly"
)
```

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

### Common error handling snippets in R

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-summary.md

Code snippets for resolving common input errors during analysis.

```r
names(x) <- ids
```

```r
BiocManager::install("org.Hs.eg.db")
```

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

### Access enrichResult data

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/types.md

Demonstrates how to run an enrichment analysis and access the resulting data table and slots.

```r
result <- enrichPathway(gene, pvalueCutoff = 0.05)

# Access result table
summary(result)           # Print summary
as.data.frame(result)     # Convert to data.frame

# Access slots
head(result@result)       # First 6 results
result@organism           # "human"
result@ontology           # "Reactome"

# Filter to significant results
sig_results <- result@result[result@result$p.adjust < 0.05, ]
```

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

### gson_Reactome

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-summary.md

Builds a GSON gene-set annotation object from Reactome.

```APIDOC
## gson_Reactome

### Description
Builds a GSON gene-set annotation object from Reactome.

### Signature
gson_Reactome(organism = "human")

### Returns
GSON
```

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

### Perform signed propagation

Source: https://github.com/yulab-smu/reactomepa/blob/devel/_autodocs/api-reference/nsePathway.md

Enables signed mode to distinguish between positive and negative seed influences.

```r
# Separate positive and negative seeds
result <- nsePathway(
  geneList,
  mode = "signed",
  pvalueCutoff = 0.05
)
```
Tokens:
Raw