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: 48.
#> Expanding; fetched 48 of 48 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 48 edges
#> #
#> # A rooted tree
#> #
#> # Node Data: 49 × 10 (active)
#> id pcategory name description synonym category iri xref namespace
#> <chr> <chr> <chr> <chr> <list> <list> <chr> <lis> <chr>
#> 1 MONDO:000… biolink:… Ehle… Arthrochal… <chr> <chr> http… <chr> MONDO
#> 2 HP:0000963 biolink:… Thin… Reduction … <chr> <chr> http… <chr> HP
#> 3 HP:0000974 biolink:… Hype… A conditio… <chr> <chr> http… <chr> HP
#> 4 HP:0001001 biolink:… Abno… NA <chr> <chr> http… <chr> HP
#> 5 HP:0001252 biolink:… Hypo… Hypotonia … <chr> <chr> http… <chr> HP
#> 6 HP:0001373 biolink:… Join… Displaceme… <chr> <chr> http… <chr> HP
#> 7 HP:0001385 biolink:… Hip … The presen… <chr> <chr> http… <chr> HP
#> 8 HP:0001387 biolink:… Join… Joint stif… <chr> <chr> http… <chr> HP
#> 9 HP:0002300 biolink:… Muti… Complete l… <chr> <chr> http… <chr> HP
#> 10 HP:0002381 biolink:… Apha… An acquire… <chr> <chr> http… <chr> HP
#> # ℹ 39 more rows
#> # ℹ 1 more variable: provided_by <chr>
#> #
#> # Edge Data: 48 × 23
#> from to subject predicate object knowledge_level negated
#> <int> <int> <chr> <chr> <chr> <chr> <lgl>
#> 1 1 2 MONDO:0007525 biolink:has_phenotype HP:00… knowledge_asse… TRUE
#> 2 1 3 MONDO:0007525 biolink:has_phenotype HP:00… knowledge_asse… TRUE
#> 3 1 4 MONDO:0007525 biolink:has_phenotype HP:00… knowledge_asse… TRUE
#> # ℹ 45 more rows
#> # ℹ 16 more variables: primary_knowledge_source <chr>,
#> # frequency_qualifier <chr>, original_subject <chr>, agent_type <chr>,
#> # knowledge_source <chr>, aggregator_knowledge_source <chr>,
#> # has_evidence <chr>, provided_by <chr>, id <chr>, category <chr>,
#> # has_total <dbl>, has_quotient <dbl>, has_count <dbl>, has_percentage <dbl>,
#> # publications <chr>, onset_qualifier <chr>
# cleanup saved file
file.remove("phenos.tar.gz")
#> [1] TRUE