clusterProfiler

https://github.com/yulab-smu/clusterprofiler
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### Usage examples for enrichWP and gseWP

Source: https://github.com/yulab-smu/clusterprofiler/blob/devel/_autodocs/api-reference/annotation-sources.md

Shows how to list WikiPathways organisms and run enrichWP and gseWP with example parameters.

```r
wp_orgs <- get_wp_organisms(); head(wp_orgs)
res <- enrichWP(de, organism = "Homo sapiens", pvalueCutoff = 0.05)
res2 <- gseWP(geneList, organism = "Mus musculus")
```

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

### NSEA usage examples

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

Examples for single-layer PPI-driven GO analysis, multi-layer KEGG analysis, and WikiPathways enrichment with error handling.

```r
# single-layer PPI-driven GO analysis
edges <- data.frame(from = c("A", "B"), to = c("B", "C"), weight = c(0.9, 0.8))
res <- nseGO(
  geneList = geneList,
  network  = edges,
  ont      = "BP",
  OrgDb    = "org.Hs.eg.db",
  mode     = "signed",
  p        = 0.6
)
if (!is.null(res)) head(as.data.frame(res))

# multi-layer: two network layers coupled by inter-layer edges
res2 <- mnseKEGG(
  seed_list = list(rna = geneList, protein = proteinScores),
  networks  = list(rna = edges_rna, protein = edges_protein),
  couplings = data.frame(from = c("node1"), to = c("node1"), layer_from = "rna", layer_to = "protein"),
  organism  = "hsa",
  collapse  = "weighted_mean",
  output_space = "union"
)

# WikiPathways network enrichment with error handling
tryCatch(
  nseWP(geneList, network = edges, organism = "Homo sapiens"),
  error = function(e) message("nseWP: ", conditionMessage(e))
)
```

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

### Example: get_wp_organisms() with enrichWP()

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

Example usage: assigns the result of get_wp_organisms() to orgs, then passes the first organism to enrichWP() for enrichment analysis.

```r
orgs <- get_wp_organisms()
enrichWP(de, organism = orgs[1])
```

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

### download_KEGG usage example

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

Example of downloading KEGG data for human (hsa) using UniProt IDs and inspecting the resulting mapping.

```r
d <- download_KEGG("hsa", "KEGG", "uniprot")
head(d$KEGGPATHID2EXTID)   # from, kegg (path id) columns
```

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

### ko2name usage example

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

Example of calling ko2name with a vector of KO IDs.

```r
ko2name(c("K04456", "K04526"))
```

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

### gseKEGG and gseMKEGG usage examples

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

Basic examples of how to call gseKEGG and gseMKEGG with specific organism and p-value cutoff parameters.

```r
res <- gseKEGG(geneList, organism = "hsa", pvalueCutoff = 0.05, seed = TRUE)
kk2 <- gseMKEGG(geneList, organism = "mmu")
```

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

### go2term() usage example

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

Example call to go2term() with two GO IDs. Returns a data.frame with matching terms from GO.db.

```r
go2term(c("GO:0006915", "GO:0008284"))
```

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

### browseKEGG usage example

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

Example of calling browseKEGG with an enrichment result object and a specific pathway ID.

```r
url <- browseKEGG(kk, "hsa04110")
```

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

### bitr_kegg() documented examples

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

Two KEGG documented examples: converting UniProt IDs to KO IDs and KO IDs to UniProt IDs for human (hsa).

```r
# KEGG documented examples
bitr_kegg(c("P31946", "P01308"), "uniprot", "ko", "hsa")
bitr_kegg(c("K04456", "K04526"), "ko", "uniprot", "hsa")
```

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

### groupGO example with gcSample and org.Hs.eg.db

Source: https://github.com/yulab-smu/clusterprofiler/blob/devel/man/groupGO.Rd

Example wrapped in \dontrun{} to prevent execution during package checks. It loads the gcSample dataset, runs groupGO on the first sample with the BP ontology at level 2, and prints the summary. The commented plot(yy) line is included as a placeholder for visualization.

```R
\dontrun{
	data(gcSample)
	yy <- groupGO(gcSample[[1]], 'org.Hs.eg.db', ont="BP", level=2)
	head(summary(yy))
	#plot(yy)
}
```

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

### gson_KEGG_mapper usage example

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

Example usage of gson_KEGG_mapper using a sample file provided within the clusterProfiler package.

```r
f <- system.file('extdata', 'kegg-mapper-blast.txt', package = 'clusterProfiler')
g <- gson_KEGG_mapper(f, format = "BLAST", type = "pathway", species = "My species")
```

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

### dropGO usage example

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

Example usage of dropGO: first enrich GO terms with enrichGO, then drop top-level terms by level, and then drop a specific term by ID.

```r
res <- enrichGO(de, OrgDb = "org.Hs.eg.db", ont = "BP")
res2 <- dropGO(res, level = 2)        # drop top-level generic BP terms
res3 <- dropGO(res2, term = "GO:0008150")  # drop "biological_process" explicitly
```

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

### gson_GO_local usage example

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

Builds a GSON from a user-supplied data frame and uses it with enricher. The example uses a single gene with multiple GO terms.

```r
mydata <- data.frame(
  gene_id = "gene1",
  go_id = c("GO:0035492", "GO:0009764", "GO:0031040", "GO:0033714", "GO:0036349")
)
g <- gson_GO_local(mydata, species = "E. coli")
res <- enricher("gene1", gson = g)
```

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

### gson_GO usage example

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

Creates a GSON for human GO BP ontology using the org.Hs.eg.db package.

```r
go_gson <- gson_GO("org.Hs.eg.db", keytype = "ENTREZID", ont = "BP")
```

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

### Usage example for groupGO()

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

Example usage of groupGO() with gcSample data and org.Hs.eg.db. Shows how to call groupGO, convert to data.frame, and use accessor functions geneID(), geneInCategory(), and show().

```r
library(clusterProfiler)
data(gcSample)
yy <- groupGO(gcSample[[1]], OrgDb = "org.Hs.eg.db", ont = "BP", level = 2)
head(as.data.frame(yy))       # as.data.frame.groupGOResult -> x@result
geneID(yy)                    # "/"-joined gene ids per term (geneID.groupGOResult)
geneInCategory(yy)            # named list: GO term -> character vector of genes
show(yy)                      # "GO BP Profiles at level 2 of 886 Homo sapiens genes"
```

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

### interpret() usage example

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

Example demonstrating how to prepare a data frame of enrichment results and call interpret() with specific model settings and PPI integration.

```r
library(clusterProfiler)
df <- data.frame(
  ID = c("GO:0006915", "GO:0008284"),
  Description = c("apoptotic process", "positive regulation of proliferation"),
  GeneRatio = c("10/100", "20/100"),
  p.adjust = c(0.01, 0.02),
  geneID = c("TP53/BAX", "MYC/CCND1/CDK4")
)

res <- interpret(df,
  model     = "deepseek:deepseek-chat",
  context   = "Cancer proliferation study",
  task      = "interpretation",
  add_ppi   = TRUE,                       # STRING evidence in the prompt
  gene_fold_change = c(TP53 = 2.1, BAX = 1.5, MYC = 3.0),
  max_tokens = 12000,
  temperature = 0.2,
  verbose   = TRUE
)
print(res)                    # print.interpretation, sections 1..7 incl. refined network
plot(res)                     # plot.interpretation -> ggplot via ggtangle
```

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

### Example calls to getTaxID and getTaxInfo

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

Example calls: getTaxID("Homo sapiens") returns "9606", and getTaxInfo("Homo sapiens", source = "ensembl")$id retrieves the Ensembl taxonomy ID.

```r
getTaxID("Homo sapiens")        # "9606"
getTaxInfo("Homo sapiens", source = "ensembl")$id
```

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

### interpret_hierarchical usage example

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

Example showing how to define a mapping between sub-clusters and major clusters, execute the hierarchical interpretation, and print the result for a specific sub-cluster.

```r
mapping <- c(Sub1 = "MajorA", Sub2 = "MajorA", Sub3 = "MajorB")
res <- interpret_hierarchical(
  x_minor = minorResult,   # per-sub-cluster enrichment
  x_major = majorResult,   # per-major-cluster enrichment
  mapping = mapping,
  model   = "gemini:gemini-2.5-flash",
  task    = "cell_type"
)
print(res[["Sub1"]])
```

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

### gofilter() usage example

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

Example call to gofilter() with an enrichGO result and level 5. The function filters the GO enrichment result to the specified GO level.

```r
res2 <- gofilter(enrichGO(de, "org.Hs.eg.db", ont = "BP"), level = 5)
```

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

### getPPI() usage examples

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

Two usage examples for getPPI(). The first queries an enriched term (ego) with taxID 9606 and returns an igraph. The second queries a protein list and returns a data.frame. Both require a valid taxID and network connectivity to STRING.

```r
g <- getPPI(ego, ID = 1, taxID = "9606", output = "igraph")
df <- getPPI(c("PTCH1", "TP53", "BRCA1", "BRCA2"), taxID = "9606", output = "data.frame")
```

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

### Run interpret_agent with a model and context

Source: https://github.com/yulab-smu/clusterprofiler/blob/devel/man/interpret_agent.Rd

Example usage of interpret_agent with a model specified as 'openai:gpt-4o' and a context describing the experimental background. The result is stored in 'res' and printed. This example is wrapped in \dontrun{} as it requires an LLM API call.

```r
res <- interpret_agent(df,
  model = "openai:gpt-4o",
  context = "scRNA-seq of mouse MI day 3"
)
print(res)
```

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

### compareCluster: basic usage with named list and formula

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

Basic examples: enrichKEGG with a named list of gene clusters, formula interface with groupGO, multi-group formula, and ranked formula against gseGO. The data frame 'mydf' is used in the formula examples. Requires the org.Hs.eg.db package for OrgDb argument.

```r
library(clusterProfiler)
data(gcSample)                     # named list of gene-cluster character vectors
xx <- compareCluster(gcSample, fun = "enrichKEGG", organism = "hsa", pvalueCutoff = 0.05)
head(as.data.frame(xx))            # Cluster, category, subcategory, ID, ...
dotplot(xx)

# formula interface
mydf <- data.frame(
  Entrez = c('1', '100', '1000', '100101467', '100127206', '100128071'),
  logFC  = c(1.1, -0.5, 5, 2.5, -3, 3),
  group  = c('A', 'A', 'A', 'B', 'B', 'B'),
  othergroup = c('good', 'good', 'bad', 'bad', 'good', 'bad')
)
xx.formula <- compareCluster(Entrez ~ group, data = mydf, fun = 'groupGO', OrgDb = 'org.Hs.eg.db')
xx.two <- compareCluster(Entrez ~ group + othergroup, data = mydf, fun = 'groupGO', OrgDb = 'org.Hs.eg.db')

# ranked formula against gseGO
xx.gsea <- compareCluster(Entrez | logFC ~ group, data = mydf, fun = 'gseGO', OrgDb = 'org.Hs.eg.db')
```

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

### Compare gene clusters with compareCluster()

Source: https://github.com/yulab-smu/clusterprofiler/blob/devel/man/compareCluster.Rd

These examples show how to use compareCluster() with a list of gene clusters and with formula interfaces. The first example uses enrichKEGG on the gcSample data and then converts the result to a data frame and plots it with dotplot(). The second and third examples use the formula interface with one or two grouping variables, respectively, and require a data frame with Entrez IDs and group columns.

```R
data(gcSample)
xx <- compareCluster(gcSample, fun="enrichKEGG",
                     organism="hsa", pvalueCutoff=0.05)
as.data.frame(xx)
# plot(xx, type="dot", caption="KEGG Enrichment Comparison")
dotplot(xx)
```

```R
## formula interface
mydf <- data.frame(Entrez=c('1', '100', '1000', '100101467',
                            '100127206', '100128071'),
                   logFC = c(1.1, -0.5, 5, 2.5, -3, 3),
                   group = c('A', 'A', 'A', 'B', 'B', 'B'),
                   othergroup = c('good', 'good', 'bad', 'bad', 'good', 'bad'))
xx.formula <- compareCluster(Entrez~group, data=mydf,
                             fun='groupGO', OrgDb='org.Hs.eg.db')
as.data.frame(xx.formula)
```

```R
## formula interface with more than one grouping variable
xx.formula.twogroups <- compareCluster(Entrez~group+othergroup, data=mydf,
                                       fun='groupGO', OrgDb='org.Hs.eg.db')
as.data.frame(xx.formula.twogroups)
```

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

### interpret_agent() usage example

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

A basic implementation of interpret_agent using an OpenAI model and specific experimental context.

```r
res <- interpret_agent(
  df,
  model   = "openai:gpt-4o",
  context = "scRNA-seq of mouse MI day 3",
  add_ppi = TRUE,
  n_pathways = 50
)
print(res$overview)
```

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

### enrichMKEGG usage example

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

Example of calling enrichMKEGG with a gene list and organism.

```r
mk <- enrichMKEGG(de, organism = "hsa")
```

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

### gseGO usage example

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

Example of running gseGO with a ranked gene list and visualizing the results with ridgeplot.

```r
data(geneList, package = "DOSE")
res <- gseGO(geneList, OrgDb = "org.Hs.eg.db", ont = "BP",
             pvalueCutoff = 0.05, seed = 2024)
ridgeplot(res)                    # re-exported from enrichplot
```

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

### Non-trivial options and error handling with enrichGO()

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

Shows using SYMBOL keyType, ont='ALL' with pool=TRUE, supplying a universe, and wrapping in tryCatch to handle errors. Also checks for NULL result and prints ONTOLOGY column.

```r
# symbols as input; readable symbols already; universe supplied; all ontologies pooled
res <- tryCatch(
  enrichGO(
    gene      = c("TP53", "BRCA1", "MYC", "CDK4"),
    OrgDb     = "org.Hs.eg.db",
    keyType   = "SYMBOL",
    ont       = "ALL",
    pool      = TRUE,
    universe  = rownames(exprMatrix),   # some background
    readable  = FALSE,
    pvalueCutoff = 0.05
  ),
  error = function(e) { message("enrichGO failed: ", conditionMessage(e)); NULL }
)

if (is.null(res)) {
  # no term passed the cutoffs, or no gene mapped in the remapping branch
} else {
  print(head(res@result$ONTOLOGY))  # BP/CC/MF column present for ont="ALL"
}
```

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

### enrichKEGG usage example

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

Example workflow for running enrichKEGG on a gene list, viewing the results, and browsing a specific pathway.

```r
library(clusterProfiler)
data(geneList, package = "DOSE")
de <- names(geneList)[1:100]

kk <- enrichKEGG(de, organism = "hsa", pvalueCutoff = 0.05)
head(as.data.frame(kk))          # category, subcategory, ID, Description, ...
browseKEGG(kk, pathID = kk$ID[1])  # opens https://www.kegg.jp/kegg-bin/show_pathway?... in browser
```

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

### Perform GO Enrichment Analysis with enrichGO

Source: https://github.com/yulab-smu/clusterprofiler/blob/devel/man/enrichGO.Rd

This example demonstrates how to perform GO enrichment analysis on a subset of genes using the org.Hs.eg.db database.

```R
  data(geneList, package = "DOSE")
	de <- names(geneList)[1:100]
	yy <- enrichGO(de, 'org.Hs.eg.db', ont="BP", pvalueCutoff=0.01)
	head(yy)
```

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

### GSEA reproducible run with GSON and error handling

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

Example demonstrating a reproducible GSEA run using a GSON object, adaptive permutation settings, and basic result validation.

```r
go_gson <- gson_GO(OrgDb = "org.Hs.eg.db", keytype = "ENTREZID", ont = "BP")

res <- GSEA(
  geneList      = geneList,
  gson          = go_gson,
  method        = "multilevel",
  nPerm         = 10000,
  adaptive      = TRUE,
  minPerm       = 200,
  maxPerm       = 20000,
  pvalThreshold = 0.05,
  seed          = 1234,          # reproducible permutation engine
  verbose       = TRUE
)

if (is.null(res)) {
  message("No gene set passed the pvalueCutoff (raw AND adjusted).")
} else {
  df <- as.data.frame(res)        # ID, Description, setSize, enrichmentScore, NES, pvalue, p.adjust, qvalues, rank, leading_edge, core_enrichment
}
```

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

### idType() usage example

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

Example call to idType() with the default human database. The comment shows the expected output format, a character vector of key types.

```r
idType("org.Hs.eg.db")   # c("ACCNUM", "ALIAS", ..., "SYMBOL", "UNIPROT", ...)
```

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

### enrichPC usage example

Source: https://github.com/yulab-smu/clusterprofiler/blob/devel/_autodocs/api-reference/annotation-sources.md

Example call to enrichPC with a character vector of HGNC symbols and a p-value cutoff of 0.05. Returns an enrichResult object or NULL when nothing passes.

```r
res <- enrichPC(c("TP53", "BRCA1", "MYC"), pvalueCutoff = 0.05)
```

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

### Basic usage of enrichGO()

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

Loads clusterProfiler, uses DOSE geneList data, selects first 100 genes, runs enrichGO with BP ontology and pvalueCutoff 0.01, then shows head of result and dotplot.

```r
library(clusterProfiler)
data(geneList, package = "DOSE")
de <- names(geneList)[1:100]

yy <- enrichGO(de, OrgDb = "org.Hs.eg.db", ont = "BP", pvalueCutoff = 0.01)
head(as.data.frame(yy))            # ID, Description, GeneRatio, BgRatio, pvalue, p.adjust, qvalue, geneID, Count
dotplot(yy)                        # re-exported from enrichplot
```

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

### Usage of gson_WP for enrichment analysis

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

Example showing how to retrieve WikiPathways data for Homo sapiens and use the resulting GSON object in an enrichment analysis.

```r
wp_gson <- gson_WP("Homo sapiens")
res <- enricher(de, gson = wp_gson)
```

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

### get_ppi_network() usage example

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

Usage example for get_ppi_network(). Downloads and caches the STRING network for taxID 9606, version 12.0, with a score threshold of 700. Returns a data.frame by default.

```r
net <- get_ppi_network(taxID = 9606, version = "12.0", score_threshold = 700)
```
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