library(mapa)3 Pathway Analysis
The mapa package offers two key functions for pathway enrichment:
enrich_pathway()for Over-Representation Analysis (ORA)do_gsea()for Gene Set Enrichment Analysis (GSEA).
Both support multiple databases and handle gene and metabolite data (metabolites currently ORA only). A unified workflow lets you analyze multiple databases simultaneously in one function call, which saves separate runs.
Important
Prerequisites: Before running enrichment analysis, ensure your data has been properly preprocessed using the convert_id() function as described in Chapter 2 - Data Input and Preprocessing. The variable_info used in this chapter should be the output from the ID conversion step.
Alternatively, you can load our example data by following 2.1.2 of Section 2.1.
3.1 Gene-based Enrichment
3.1.1 Supported Databases and Keytypes
The mapa package leverages the powerful enrichment functions from the clusterProfiler package for pathway analysis. The following table shows which databases you can use for your organism and the supported key types:
| Database | Supported Organism | Keytype Options |
|---|---|---|
| Gene Ontology (GO) | Any organism with OrgDb object (organism annotation database package from Bioconductor or OrgDb object retrieved from AnnotationHub, see Section 2.2.1) | Any keyType supported by your OrgDb object |
| KEGG | All KEGG organisms | "kegg", "ncbi-geneid", "ncbi-proteinid", "uniprot" |
| Reactome | human, rat, mouse, celegans, yeast, zebrafish, fly, bovine, canine, chicken | ENTREZID only |
Tip
To check available GO keytype for your organism:
For model organisms with Bioconductor annotation packages
## For model organisms with Bioconductor annotation packages library(org.Mm.eg.db) AnnotationDbi::keytypes(org.Mm.eg.db) # [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS" "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME" # [11] "GENETYPE" "GO" "GOALL" "IPI" "MAP" "OMIM" "ONTOLOGY" "ONTOLOGYALL" "PATH" "PFAM" # [21] "PMID" "PROSITE" "REFSEQ" "SYMBOL" "UCSCKG" "UNIPROT"For non-model organisms with annotation OrgDb retrieved from AnnotationHub
# variable_info is the output from the ID conversion step in Chapter 2 - Data Input and Preprocessing AnnotationDbi::keytypes(variable_info$orgdb)For comprehensive understanding of enrichment analysis concepts, methodologies, we highly recommend reading the Biomedical Knowledge Mining using GOSemSim and clusterProfiler book. This book covers all the underlying methods that MAPA uses and will help you make informed decisions about your analysis parameters.
3.1.2 Basic Usage
Use enrich_pathway() for Over-Representation Analysis.
library(org.Mm.eg.db)
gene_enriched_pathways <-
enrich_pathway(
variable_info = variable_info,
query_type = "gene",
database = c("go", "kegg", "reactome"),
# GO parameters
go.orgdb = org.Mm.eg.db, # or "org.Mm.eg.db" (in quotes) if you did not load `org.Mm.eg.db`
go.keytype = "ENTREZID",
go.ont = "ALL",
# KEGG parameters
kegg.organism = "mmu",
kegg.keytype = "kegg",
# Reactome parameters
reactome.organism = "mouse",
# Statistical parameters
pvalueCutoff = 0.05,
pAdjustMethod = "BH"
)
# GO database...
# KEGG database...
# Reading KEGG annotation online: "https://rest.kegg.jp/link/hsa/pathway"...
# Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/hsa"...
# Reactome database...
# Done.The do_gsea() function performs GSEA using ranked gene lists based on fold changes or other metrics.
library(org.Mm.eg.db)
gsea_pathways <-
do_gsea(
variable_info = variable_info,
query_type = "gene",
order_by = "fc", # Column to rank genes by
database = c("go", "kegg", "reactome"),
# Database parameters
go.orgdb = org.Mm.eg.db, # or "org.Mm.eg.db" (in quotes) if you did not load `org.Mm.eg.db`
go.ont = "ALL",
go.keytype = "ENTREZID",
kegg.organism = "mmu",
kegg.keytype = "kegg",
reactome.organism = "mouse",
# GSEA parameters
pvalueCutoff = 0.05,
pAdjustMethod = "BH"
)
Note
- Model organisms: use
variable_infodirectly and package name forgo.orgdb - Non-model organisms: use
variable_info$datafor parametervariable_infoandvariable_info$orgdbforgo.orgdb
3.2 Metabolite-based Enrichment
3.2.1 Supported Databases and Keytypes
| Database | Supported Organism | Keytype Options |
|---|---|---|
| KEGG | All KEGG organisms | KEGG compound IDs |
| SMPDB | Only for human | HMDB IDs |
3.2.2 Basic Usage
For metabolite data, ensure your variable_info contains HMDB IDs and/or KEGG compound IDs.
met_enriched_pathways <-
enrich_pathway(
variable_info = met_variable_info,
query_type = "metabolite",
database = c("hmdb", "metkegg"),
met_organism = "hsa",
save_to_local = TRUE,
pvalueCutoff = 0.05,
pAdjustMethod = "BH"
)3.3 Results interpretation
After enrichment analysis, view the enrichment analysis summary:
gene_enriched_pathways
# --------------------
# Analysis method: enrich_pathway
# --------------------
# -----------Variable information------------
# 66 features/markers in total
# -----------Enrichment results and modules of genes------------
# -----------GO------------
# 197 GO terms with p.adjust < 0.05
# No GO modules
# -----------KEGG------------
# 30 KEGG pathways with p.adjust < 0.05
# No KEGG modules
# -----------Reactome------------
# 16 Reactome pathways with p.adjust < 0.05
# No Reactome modules
# -----------Enrichment results and modules of metabolites------------
# -----------HMDB------------
# No HMDB results
# No HMDB modules
# -----------KEGG Metabolite------------
# No KEGG metabolite results
# No KEGG modules
# -----------Functional modules------------
# No Functional modules
# -----------LLM module interpretation------------
# No LLM module interpretation results
# --------------------
# Processing information
# 1 processings in total
# enrich_pathway ----------
# Package Function.used Time
# 1 mapa enrich_pathway() 2025-09-20 12:26:40You can access specific database results via @:
head(gene_enriched_pathways@enrichment_go_result@result)
# ONTOLOGY ID Description GeneRatio
# GO:0015980 BP GO:0015980 energy derivation by oxidation of organic compounds 12/63
# GO:0009060 BP GO:0009060 aerobic respiration 9/63
# GO:0045333 BP GO:0045333 cellular respiration 9/63
# GO:0003012 BP GO:0003012 muscle system process 10/63
# GO:0006941 BP GO:0006941 striated muscle contraction 7/63
# GO:0006936 BP GO:0006936 muscle contraction 8/63
# BgRatio RichFactor FoldEnrichment zScore pvalue p_adjust qvalue
# GO:0015980 380/28928 0.03157895 14.500251 12.376048 3.244556e-11 3.912934e-08 2.759580e-08
# GO:0009060 206/28928 0.04368932 20.061026 12.826517 6.707680e-10 4.044731e-07 2.852529e-07
# GO:0045333 271/28928 0.03321033 15.249341 11.010328 7.404865e-09 2.976756e-06 2.099344e-06
# GO:0003012 460/28928 0.02173913 9.982057 9.072196 5.645295e-08 1.702057e-05 1.200368e-05
# GO:0006941 181/28928 0.03867403 17.758134 10.565860 1.375932e-07 3.318748e-05 2.340533e-05
# GO:0006936 329/28928 0.02431611 11.165340 8.663245 5.781200e-07 1.065983e-04 7.517800e-05
# geneID Count
# GO:0015980 66128/66043/94044/12833/66576/12867/78920/16828/78330/407785/102093/19045 12
# GO:0009060 66128/66043/94044/12833/66576/12867/78920/78330/407785 9
# GO:0045333 66128/66043/94044/12833/66576/12867/78920/78330/407785 9
# GO:0003012 11640/11464/12313/226594/12833/26399/17885/407785/56012/74166 10
# GO:0006941 11464/12313/226594/26399/17885/56012/74166 7
# GO:0006936 11464/12313/226594/26399/17885/407785/56012/74166 8head(gsea_pathways@enrichment_kegg_result@result)
# ID Description setSize enrichmentScore NES
# mmu05322 mmu05322 Systemic lupus erythematosus 17 0.4629405 2.010801
# mmu04664 mmu04664 Fc epsilon RI signaling pathway 16 0.4603239 1.973294
# mmu04141 mmu04141 Protein processing in endoplasmic reticulum 44 -0.4963270 -1.685713
# mmu05014 mmu05014 Amyotrophic lateral sclerosis 68 -0.4359574 -1.547397
# pvalue p_adjust qvalue rank leading_edge
# mmu05322 0.0005485788 0.04951072 0.04812464 1343 tags=100%, list=54%, signal=46%
# mmu04664 0.0008427356 0.04951072 0.04812464 1349 tags=100%, list=54%, signal=46%
# mmu04141 0.0005266381 0.04951072 0.04812464 991 tags=77%, list=40%, signal=47%
# mmu05014 0.0007372888 0.04951072 0.04812464 878 tags=63%, list=35%, signal=42%
# core_enrichment
# mmu05322 14960/14969/14961/12262/13035/326619/15078/109711/15270/26914/60595/12268/20821/20641/67332/50909/20823
# mmu04664 19354/20963/234779/18783/22324/18707/16331/11651/18750/26417/17096/14784/19353/16653/22325/26416
# mmu04141 19089/27061/68292/67075/69276/26408/26965/320011/20014/12333/27054/20224/56228/216440/14827/23802/22027/100037258/99683/50907/110379/103963/81500/22393/50527/66967/66212/12304/12330/81489/71853/14376/18453/67397
# mmu05014 26408/69654/53319/17184/71844/227197/107939/56480/227699/59288/225887/68342/59015/433702/17274/53598/53857/234865/108989/230908/269966/53379/56717/67665/69912/19069/56208/16573/237782/74764/110379/70699/19132/226977/214585/12864/67680/445007/11744/19172/11750/225326/103468
# Count
# mmu05322 17
# mmu04664 16
# mmu04141 34
# mmu05014 43Key columns in enrichment results:
Core Information:
ID: Pathway identifier (e.g., GO:0042060 for GO, hsa04060 for KEGG, R-HSA-5669034 for Reactome)Description: Pathway nameONTOLOGY: Biological ontology (for GO only: MF=molecular function, CC=cellular component, BP=biological process)category/subcategory: KEGG pathway categories (for KEGG only)
Statistical Measures:
pvalue: Raw p-value from hypergeometric test (equivalent to one-sided Fisher’s exact test)p_adjust: Adjusted p-value after multiple testing correction (BH method by default)qvalue: Q-value for FDR control. For more information, see?qvalue::qvalue
Gene Mapping:
GeneRatio: Ratio of input genes annotated to this pathway (format: “genes_in_pathway/total_input_genes”)BgRatio: Ratio of all genes annotated to this pathway in the background universe (format: “pathway_genes/universe_genes”)Count: Total number of genes from input list that match this pathwaygeneID: Gene IDs that overlap between your gene list and the pathway (separated by “/”)
Enrichment Metrics:
RichFactor: Ratio of input genes annotated to a pathway vs. all genes annotated to this pathwayFoldEnrichment: Enrichment fold change (GeneRatio divided by BgRatio)zScore: Standard deviations away from expected overlap (How unusual or extreme the observed enrichment is compared to what you’d expect by chance. Higher absolute z-score means more “surprising” or significant enrichment)
For GSEA results, additional columns specific to ranked list analysis include:
setSize: Total number of genes in the gene set/pathwayenrichmentScore: Degree of overrepresentation at top/bottom of ranked listNES: Normalized Enrichment Score - main metric for interpretation- Positive NES: pathway enriched in upregulated genes (pathway activation)
- Negative NES: pathway enriched in downregulated genes (pathway suppression)
rank: Position in ranked list where maximum enrichment score occurredleading_edge: Statistics for the leading-edge subset of genescore_enrichment: Core enriched genes that contribute most to the enrichment signalCount: Number of core enriched genes
For metabolite ORA results, the result structure differs from gene-based analysis:
Pathway Information:
pathway_id: Small Molecule Pathway Database Pathway identifier (e.g., SMP0000028)pathway_name: Name of the metabolic pathway (e.g., “Caffeine Metabolism”)describtion: Detailed description of the pathway from the corresponding databasepathway_class: Classification of pathway (e.g., “Metabolic;primary_pathway”, “Disease;primary_pathway”)
Statistical Measures:
p_value: Raw p-value from enrichment testp_adjust: Adjusted p-value for multiple testing correction
Metabolite Mapping:
all_id: All metabolite IDs in this pathway (separated by “;”)all_number: Total number of metabolites in the pathwaymapped_id: Your input metabolite IDs that map to this pathway (separated by “;”)mapped_number: Number of your metabolites that map to this pathwaymapped_percentage: Percentage of pathway metabolites covered by your input data
The enrichment results object is tailored for downstream similarity analysis and module identification in MAPA, so it cannot be substituted with outputs from other tools. Please make sure you complete the steps above before proceeding.