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Reactome Over-Representation or Expression Analysis

Source: R/reactome_analysis.R
rba_reactome_analysis.Rd

Using this function, you can perform Reactome Analysis In a convenient way. The Analysis Type will be chosen depending on your supplied input:

  1. If you supply a vector or a single-columned table, "Over-Representation" analysis will be performed.

  2. If you supply a multi-column table, with the first column being molecules identifiers and the rest being numeral expression values, "Expression" analysis will be performed.

See the details section for the accepted input types and format.

Usage

rba_reactome_analysis(
  input,
  input_format = NULL,
  projection = TRUE,
  interactors = FALSE,
  species = NULL,
  sort_by = "ENTITIES_PVALUE",
  order = "ASC",
  resource = "TOTAL",
  p_value = 1,
  include_disease = TRUE,
  min = NULL,
  max = NULL,
  ...
)

Arguments

input

Character or Numeric vector, Data frame, or Matrix: A vector, data frame, matrix or a local file path or URL that points to your data. See "Details section" for more information of how to organize and supply your input.

input_format

Character: (optional) This function will automatically identify your supplied input's format. But in case of unexpected issues or if you want to be explicit, set this argument to one of:

  • "table": If you supplied a data frame or matrix as input.

  • "vector": If you supplied a simple vector (numeric or character) as input.

  • "file": If you supplied a local file path pointing to a correctly-formatted text file.

  • "url": If you supplied an HTTP or HTTPS URL pointing to a correctly-formatted text file.

An explicit value takes precedence. Otherwise, HTTP and HTTPS addresses are identified first, followed by existing local files, tables, and then other non-empty character or numeric inputs as identifier vectors.

projection

Logical: (default = TRUE) Should non-human identifiers be projected to their human equivalents? (using Reactome orthology data)

interactors

Logical: (default = FALSE) Should IntAct interaction data be used to increase the analysis background?

species

Character or Numeric: (optional) NCBI Taxonomy identifier (Human is 9606), species name (e.g. "Homo sapiens") or Reactome DbId (e.g Homo sapiens is 48887). See rba_reactome_species or Reactome Data Schema: Entries: Species. Note that you cannot supply the species parameter when projection parameter is TRUE.

sort_by

Character: (default = "ENTITIES_PVALUE") Sort the result based on what column? Available choices are: "NAME", "TOTAL_ENTITIES", "TOTAL_INTERACTORS", "TOTAL_REACTIONS", "FOUND_ENTITIES", "FOUND_INTERACTORS", "FOUND_REACTIONS", "ENTITIES_RATIO", "ENTITIES_PVALUE", "ENTITIES_FDR" or "REACTIONS_RATIO"

order

Character: (default = "ASC") Sort Order. Can be either "ASC" or "DESC".

resource

Character: (default = "TOTAL") Filter results based on the resource. Available choices are: "TOTAL", "UNIPROT", "ENSEMBL", "CHEBI", "IUPHAR", "MIRBASE", "NCBI_PROTEIN", "EMBL", "COMPOUND" or "PUBCHEM_COMPOUND".

p_value

Numeric: (default = 1) Set a P value threshold. Only results with P value equal to or less than your supplied threshold will be returned (1 means no P value filtering).

include_disease

Logical: (default = TRUE) Should the disease pathways be included in the results?

min

Numeric: (optional) Minimum number of entities that a pathways should have to be included in the results.

max

Numeric: (optional) Maximum number of entities that a pathways should have to be included in the results.

...

rbioapi option(s). See rba_options's arguments manual for more information on available options.

Value

A list containing the results and information about the analysis. The pathways element is a data frame with information about each pathway expanded into columns; it is an empty data frame when no pathways match. The token in results$summary$token can be used to retrieve the results later or in other Reactome analysis functions.

Details

You can supply your table or vector input in numerous formats:

  1. An R object which can be a data frame, matrix, or simple vector.

  2. A path to a local text file in your device that contains the molecules data. (The file should be formatted correctly, see below.)

  3. An HTTP or HTTPS URL pointing to a text file on the web that contains the molecules data. (The file should be formatted correctly, see below.)

If you supply a text file (as a local file path or URL), it should be in TSV (Tab-Separated Values) format; the first column name should start with "#". Note that if you are providing the file for "Over-Representation" analysis (i.e. Single columned-data) this header line is optional and will be used as your 'Sample Name', otherwise it is required.
Also, form the "summary" element in the function's output, you can see how Reactome Interpreted your input and subsequently the type of analysis that has been performed.
There is no strict criteria about the type of your molecules Identifiers, Reactome will Map the IDs to it's internal database entities. Nevertheless, You can check if all your identifiers has been found in "identifiersNotFound" element in the function's output.
After any analysis, Reactome will associate a token with your analysis. It can later be used in functions that require the token (e.g. to retrieve the analysis results, download pdf).
Note that Reactome will store your token for only 7 days. You can download your full results with rba_reactome_analysis_download, and re-import it anytime to reactome (using rba_reactome_analysis_import) to generate a new token.

Corresponding API Resources

"POST https://reactome.org/AnalysisService/identifiers/form"
"POST https://reactome.org/AnalysisService/identifiers/form/projection"
"POST https://reactome.org/AnalysisService/identifiers/url"
"POST https://reactome.org/AnalysisService/identifiers/url/projection"

References

  • Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

  • Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

  • Reactome Analysis Services API Documentation

  • Citations note on Reactome website

See also

Other "Reactome Analysis Service": rba_reactome_analysis_download(), rba_reactome_analysis_import(), rba_reactome_analysis_mapping(), rba_reactome_analysis_pdf(), rba_reactome_analysis_species(), rba_reactome_analysis_token()

Other "Enrichment/Over-representation": rba_enrichr(), rba_mieaa_enrich(), rba_panther_enrich(), rba_string_enrichment(), rba_string_enrichment_image()

Examples

if (FALSE) { # \dontrun{
rba_reactome_analysis(input = c("p53", "BRCA1", "cdk2", "Q99835", "CDC42"))
} # }
if (FALSE) { # \dontrun{
rba_reactome_analysis(input = "c:/rbioapi/genes.txt")
} # }
if (FALSE) { # \dontrun{
rba_reactome_analysis(input = "https://example.com/genes.txt")
} # }