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Gene Set and Pathway Enrichment

See the FAQ for dependencies and return conventions.

1) Run gene set enrichment against an Enrichr library

  • Purpose: Run gene set enrichment against an Enrichr library.
  • Method: Use EnrichrExt to query or submit the supplied input and parse the response.
  • API： dnakit.databases.enrichr_enrich(*, operation=None, gene_list, library=None, top_n=None, species=None, verbose=True)
  • Input: Provide gene_list.
  • Output: Returns enriched terms, p-values, z-scores, combined scores and overlapping genes.
  • Example code:
import dnakit

result = dnakit.databases.enrichr_enrich(
    gene_list=['TP53', 'BRCA1', 'EGFR', 'KRAS', 'MYC', 'BRAF', 'PTEN'],
    library='GO_Biological_Process_2023',
    top_n=5,
    verbose=False,
)
print(result)
  • Example output: Returns enriched terms, p-values, z-scores, combined scores and overlapping genes.

Full parameters and usage notes

2) Retrieve connections between genes and enriched pathways

  • Purpose: Retrieve connections between genes and enriched pathways.
  • Method: Use Enrichr to query or submit the supplied input and parse the response.
  • API： dnakit.databases.enrichr_gene_enrichment_analysis(*, gene_list, libs=None, verbose=True)
  • Input: Provide gene_list.
  • Output: Returns a mapping with connected_paths, connections and related fields.
  • Example code:
import dnakit

result = dnakit.databases.enrichr_gene_enrichment_analysis(
    gene_list=['TP53', 'BRCA1', 'EGFR'],
    libs=['KEGG_2021_Human'],
    verbose=False,
)
print(result)
  • Example output: The response has the following shape; records depend on the input and source.

Response-shape excerpt; <…> denotes type placeholders, not a captured query result.

{
  "connected_paths": "<object>",
  "connections": "<object>"
}

Full parameters and usage notes

3) Run PANTHER gene set overrepresentation analysis

  • Purpose: Run PANTHER gene set overrepresentation analysis.
  • Method: Use PANTHER to query or submit the supplied input and parse the response.
  • API： dnakit.databases.panther_enrichment(*, gene_list, organism=None, annotation_dataset=None, verbose=True)
  • Input: Provide gene_list.
  • Output: Returns a mapping with gene_list, organism, annotation_dataset, result_count and related fields.
  • Example code:
import dnakit

result = dnakit.databases.panther_enrichment(
    gene_list='TP53,BRCA1,EGFR,KRAS,PTEN',
    organism=9606,
    annotation_dataset='GO:0008150',
    verbose=False,
)
print(result)
  • Example output: The response has the following shape; records depend on the input and source.

Response-shape excerpt; <…> denotes type placeholders, not a captured query result.

{
  "gene_list": "<string>",
  "organism": "<integer>",
  "annotation_dataset": "<string>",
  "result_count": "<integer>",
  "enriched_terms": [
    {
      "term_id": "<string>",
      "term_label": "<string>",
      "number_in_list": "<integer>",
      "number_in_reference": "<integer>",
      "expected": "<number>",
      "fold_enrichment": "<number>",
      "pvalue": "<number>",
      "fdr": "<number>"
    }
  ]
}

Full parameters and usage notes

4) Run Reactome pathway enrichment analysis

  • Purpose: Run Reactome pathway enrichment analysis.
  • Method: Use ReactomeAnalysis to query or submit the supplied input and parse the response.
  • API： dnakit.databases.reactome_analysis_pathway_enrichment(*, identifiers, page_size=None, include_disease=None, projection=None, verbose=True)
  • Input: Provide identifiers.
  • Output: Returns a mapping with token, analysis_type, projection, identifiers_not_found and related fields.
  • Example code:
import dnakit

result = dnakit.databases.reactome_analysis_pathway_enrichment(
    identifiers='TP53\nBRCA1\nCDH1\nEGFR\nKRAS',
    verbose=False,
)
print(result)
  • Example output: The response has the following shape; records depend on the input and source.

Response-shape excerpt; <…> denotes type placeholders, not a captured query result.

{
  "token": "<string>",
  "analysis_type": "<string>",
  "projection": "<boolean>",
  "identifiers_not_found": "<integer>",
  "pathways_found": "<integer>",
  "pathways": [
    {
      "pathway_id": "<string>",
      "name": "<string>",
      "species": "<string>",
      "is_disease": "<boolean>",
      "is_lowest_level": "<boolean>",
      "entities_found": "<integer>",
      "entities_total": "<integer>",
      "entities_coverage": "<number>"
    }
  ]
}

Full parameters and usage notes

5) Analyze functional and pathway enrichment for a protein set

  • Purpose: Analyze functional and pathway enrichment for a protein set.
  • Method: Use STRING to query or submit the supplied input and parse the response.
  • API： dnakit.databases.string_functional_enrichment(*, protein_ids, species=None, category=None, verbose=True)
  • Input: Provide protein_ids.
  • Output: Returns a list of records; each record includes category, term, number_of_genes, number_of_genes_in_background and related fields.
  • Example code:
import dnakit

result = dnakit.databases.string_functional_enrichment(
    protein_ids=['TP53', 'MDM2', 'ATM', 'CHEK2', 'BRCA1'],
    species=9606,
    category='Process',
    verbose=False,
)
print(result)
  • Example output: The response has the following shape; records depend on the input and source.

Response-shape excerpt; <…> denotes type placeholders, not a captured query result.

[
  {
    "category": "<string>",
    "term": "<string>",
    "number_of_genes": "<integer>",
    "number_of_genes_in_background": "<integer>",
    "ncbiTaxonId": "<integer>",
    "inputGenes": "<string>",
    "preferredNames": "<string>",
    "p_value": "<number>"
  }
]

Full parameters and usage notes

6) Run STRING enrichment from newline-separated protein identifiers

  • Purpose: Run STRING enrichment from newline-separated protein identifiers.
  • Method: Use API to query or submit the supplied input and parse the response.
  • API： dnakit.databases.string_get_functional_enrichment(*, identifiers, species=9606, category=None, verbose=True)
  • Input: Provide identifiers.
  • Output: Returns STRING_functional_enrichment results; see string_functional_enrichment.
  • Example code:
import dnakit

result = dnakit.databases.string_get_functional_enrichment(
    identifiers='TP53\nBRCA1\nBRCA2',
    species=9606,
    verbose=False,
)
print(result)

Full parameters and usage notes