When you want to get to know and love your data

Enrichment Network


Enrichment is beyond random occurrence within a category. Networks can represent relationships among variables. Enrichment networks display relationships among variables which are over represented compared to random chance.


Next is  a tutorial for making enrichment networks for biological (metabolomic) data in R using the KEGG database.

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4 responses

  1. Sir I could reproduce all figures except the last, can you please help

    We could also map metabolites on a large scale using pathway.id= 01100.

    map <- gsub("path:ath", "", names(pathways)[1]) # remove 'path:ath'
    pv.out <- pathview(gene.data = gene.data2, cpd.data = metabolite.data2, gene.idtype = "UNIPROT",
    pathway.id = 01100, species = "hsa", out.suffix = map, keys.align = "y", kegg.native = T,
    match.data = T, key.pos = "topright")
    plot.name <- paste(map, map, "multi", "png", sep = ".")

    May 12, 2014 at 4:24 am

    • There was typo in the code: pathway.id = 01100 should be: pathway.id = “01100” (fixed)
      As an aside, the rendering process for large maps can take a long time.

      May 12, 2014 at 4:41 pm

  2. Pawan

    I would like to know if there is any specific identifier that pathview uses from mapping metabolites. I have a list of metabolites (Example: Ala, Val, Gly), I am not sure if they need to be converted to any specific identifiers before mapping to KEGG. Any ideas would be much appreciated.

    June 25, 2015 at 4:24 pm

    • KEGG is the preferred identifier in pathview.

      Looking at the documentation of path view:
      library(pathview)
      data(cpd.simtypes)
      cpd.simtypes

      There are 7 other types of identifiers which are accepted like CAS and PubChem.
      Starting with chemical names (e.g. Ala, Val, Gly) is a challenge to translate. I would use a service like the chemical translation service (http://cts.fiehnlab.ucdavis.edu/) to translate ‘chemical name’ to KEGG.

      June 27, 2015 at 2:40 pm

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