# Obtaining PIN and Gene Sets Data

#### 2023-10-08

# Get PIN File

For retrieving the PIN file for an organism of your choice, you may use the function `get_pin_file()`. As of this version, the only source for PIN data is “BioGRID”.

By default, the function downloads the PIN data from BioGRID and processes it, saves it in a temporary file and returns the path:

    ## the default organism is "Homo_sapiens"
    path_to_pin_file <- get_pin_file()

You can retrieve the PIN data for the organism of your choice, by setting the `org` argument:

    ## retrieving PIN data for "Gallus_gallus"
    path_to_pin_file <- get_pin_file(org = "Gallus_gallus")

You may also supply a `path/to/PIN/file` to save the PIN file for later use (in this case, the path you supply will be returned):

    ## saving the "Homo_sapiens" PIN as "/path/to/PIN/file"
    path_to_pin_file <- get_pin_file(path2pin = "/path/to/PIN/file")

You may also retrieve a specific version of BioGRID via setting the `release` argument:

    ## retrieving PIN data for "Mus_musculus" from BioGRID release 3.5.179
    path_to_pin_file <- get_pin_file(
      org = "Mus_musculus",
      release = "3.5.179"
    )

# Get Gene Sets List

To retrieve organism-specific gene sets list, you may use the function `get_gene_sets_list()`. The available sources for gene sets are “KEGG”, “Reactome” and “MSigDB”. The function retrieves the gene sets data from the source and processes it into a list of two objects used by pathfindR for active-subnetwork-oriented enrichment analysis: 1. gene_sets A list containing the genes involved in each gene set 2. descriptions A named vector containing the descriptions for each gene set

By default, `get_gene_sets_list()` obtains “KEGG” gene sets for “hsa”.

## KEGG Pathway Gene Sets

To obtain the gene sets list of the KEGG pathways for an organism of your choice, use the KEGG organism code for the selected organism. For a full list of all available organisms, see here.

    ## obtaining KEGG pathway gene sets for Rattus norvegicus (rno)
    gsets_list <- get_gene_sets_list(org_code = "rno")

## Reactome Pathway Gene Sets

For obtaining Reactome pathway gene sets, set the `source` argument to “Reactome”. This downloads the most current Reactome pathways in gmt format and processes it into the list object that pathfindR uses:

    gsets_list <- get_gene_sets_list(source = "Reactome")

For Reactome, there is only one collection of pathway gene sets.

## MSigDB Gene Sets

Using `msigdbr`, `pathfindR` can retrieve all MSigDB gene sets. For this, set the `source` argument to “MSigDB” and the `collection` argument to the desired MSigDB collection (one of H, C1, C2, C3, C4, C5, C6, C7):

    gsets_list <- get_gene_sets_list(
      source = "MSigDB",
      collection = "C2"
    )

The default organism for MSigDB is “Homo sapiens”, you may obtain the gene sets data for another organism by setting the `species` argument:

    ## obtaining C5 gene sets data for "Drosophila melanogaster"
    gsets_list <- get_gene_sets_list(
      source = "MSigDB",
      species = "Drosophila melanogaster",
      collection = "C5"
    )

    ## see msigdbr::msigdbr_show_species() for all available organisms
    msigdbr::msigdbr_show_species()
    #> Warning: 'msigdbr::msigdbr_show_species' is deprecated.
    #> Use 'msigdbr_species' instead.
    #> See help("Deprecated")
    #>  [1] "Anolis carolinensis"             "Bos taurus"
    #>  [3] "Caenorhabditis elegans"          "Canis lupus familiaris"
    #>  [5] "Danio rerio"                     "Drosophila melanogaster"
    #>  [7] "Equus caballus"                  "Felis catus"
    #>  [9] "Gallus gallus"                   "Homo sapiens"
    #> [11] "Macaca mulatta"                  "Monodelphis domestica"
    #> [13] "Mus musculus"                    "Ornithorhynchus anatinus"
    #> [15] "Pan troglodytes"                 "Rattus norvegicus"
    #> [17] "Saccharomyces cerevisiae"        "Schizosaccharomyces pombe 972h-"
    #> [19] "Sus scrofa"                      "Xenopus tropicalis"

You may also obtain the gene sets for a subcollection by setting the `subcollection` argument:

    ## obtaining C3 - MIR: microRNA targets
    gsets_list <- get_gene_sets_list(
      source = "MSigDB",
      collection = "C3",
      subcollection = "MIR"
    )