Given a graph, saves it using the tabular KGX format
(see https://github.com/biolink/kgx/blob/master/specification/kgx-format.md) for later
use with load_kgx() (or even backing an engine with file_engine()). Note that if
any engine is associated with the graph it is not saved.
save_kgx(graph, filename = "saved_kgx_graph.tar.gz", ...)The input graph (invisibly).
phenos <- monarch_engine() |>
fetch_nodes(query_ids = "MONDO:0007525") |>
expand(predicates = "biolink:has_phenotype",
categories = "biolink:PhenotypicFeature")
#> Trying to connect to https://neo4j.monarchinitiative.org
#> Connected to https://neo4j.monarchinitiative.org
#> Fetching; counting matching nodes...
#> total: 1.
#> Fetching; fetched 1 of 1
#> Expanding; counting matching edges...
#> total: 52.
#> Expanding; fetched 52 of 52 edges.
save_kgx(phenos, "phenos.tar.gz")
# when loading the graph, we can optionally attach an engine
loaded_phenos <- load_kgx("phenos.tar.gz", attach_engine = monarch_engine())
#> Trying to connect to https://neo4j.monarchinitiative.org
#> Connected to https://neo4j.monarchinitiative.org
loaded_phenos
#> # A tbl_graph: 49 nodes and 52 edges
#> #
#> # A directed acyclic multigraph with 1 component
#> #
#> # Node Data: 49 × 15 (active)
#> id pcategory name description synonym category iri xref
#> <chr> <chr> <chr> <chr> <list> <list> <chr> <lis>
#> 1 MONDO:0007525 biolink:Disease Ehle… An inherit… <chr> <chr> http… <chr>
#> 2 HP:0001058 biolink:Phenoty… Poor… A reduced … <chr> <chr> http… <chr>
#> 3 HP:0001476 biolink:Phenoty… Dela… A delay in… <chr> <chr> http… <chr>
#> 4 HP:0001252 biolink:Phenoty… Hypo… Hypotonia … <chr> <chr> http… <chr>
#> 5 HP:0001075 biolink:Phenoty… Atro… Scars that… <chr> <chr> http… <chr>
#> 6 HP:0001290 biolink:Phenoty… Gene… Generalize… <chr> <chr> http… <chr>
#> 7 HP:0005191 biolink:Phenoty… Cong… NA <chr> <chr> http… <chr>
#> 8 HP:0009540 biolink:Phenoty… Cont… Chronic lo… <chr> <chr> http… <chr>
#> 9 HP:0000938 biolink:Phenoty… Oste… Osteopenia… <chr> <chr> http… <chr>
#> 10 HP:0001822 biolink:Phenoty… Hall… Lateral de… <chr> <chr> http… <chr>
#> # ℹ 39 more rows
#> # ℹ 7 more variables: related_synonyms <list>, narrow_synonyms <list>,
#> # exact_synonyms <list>, namespace <chr>, provided_by <chr>, subsets <list>,
#> # broad_synonyms <list>
#> #
#> # Edge Data: 52 × 22
#> from to subject predicate object primary_knowledge_so…¹ knowledge_level
#> <int> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1 2 MONDO:000… biolink:… HP:00… infores:omim knowledge_asse…
#> 2 1 3 MONDO:000… biolink:… HP:00… infores:omim knowledge_asse…
#> 3 1 4 MONDO:000… biolink:… HP:00… infores:omim knowledge_asse…
#> # ℹ 49 more rows
#> # ℹ abbreviated name: ¹primary_knowledge_source
#> # ℹ 15 more variables: negated <lgl>, original_subject <chr>, agent_type <chr>,
#> # aggregator_knowledge_source <chr>, has_evidence <chr>, provided_by <chr>,
#> # id <chr>, category <chr>, has_quotient <dbl>, has_total <dbl>,
#> # has_count <dbl>, has_percentage <dbl>, publications <chr>,
#> # onset_qualifier <chr>, frequency_qualifier <chr>
# cleanup saved file
file.remove("phenos.tar.gz")
#> [1] TRUE