vignettes/phenotype-matrix-services.Rmd
phenotype-matrix-services.RmdIn this example, we’re going to explore the capabilities of the
phenotype matrix services using the gorr package.
First load the gorr package, the tidyverse
package is recommended in general, but not required for this example
First we’ll need to establish a connection to our API services. To do
that we’ll need to call platform_connect and provide it
with the relevant parameters pointing to the phenotype-catalog-service,
i.e. api_key and project:
conn <- platform_connect(api_key = Sys.getenv("GOR_API_KEY"),
project = Sys.getenv("GOR_API_PROJECT"))
conn
#> ── GOR API service connection ──────────────────────────────────────────────────
#> • Service Root/s: https://platform.wuxinextcodedev.com/api/query, https://platform.wuxinextcodedev.com/api/phenotype-catalog, https://platform.wuxinextcodedev.com/queryserver, https://platform.wuxinextcodedev.com/workflow
#> • Project: ukbb_hg38
#> • API key issued at: 2022-05-18 10:18:54
#> • API key expires at: Never
#> • Access token issued at: 2022-06-16 15:56:01
#> • Access token expires at: 2022-06-17 15:56:01If everything goes as planned, we’ll have a conn object
to pass into subsequent functions.
Let’s start by listing available phenotypes in project with “Cases” as one of its tags
recipe_tag = "Cases"
phenos<- get_phenotypes(conn, any_tags = recipe_tag, limit = 20)
phenos[1:3]
#> $phecode_case_180.3
#> ── Phenotype ───────────────────────────────────────────────────────────────────
#> $name: phecode_case_180.3
#> $description: Cervical intraepithelial neoplasia [CIN] [Cervical dysplasia]
#> $result_type: SET
#> $tag_list: Cases, Phecode, judge, pair
#> $pn_count: 2081
#> $query:
#>
#> $garpur_test_153
#> ── Phenotype ───────────────────────────────────────────────────────────────────
#> $name: garpur_test_153
#> $description: All results from <a href="https://ruv.is">ruv</a> random sample 200k plink 2.5 word soup long description for a phenotype catalog entry that we want to see what happens when it's really really long. Like super long. Like longer than usual.
#> $result_type: CATEGORY
#> $tag_list: Cases, garpur_test_tag_1, physical_measurements
#> $pn_count: 200643
#> $query: nor freezes/ukbb_wes200k_plink25/buckets.tsv
#> | calc value if(random()>0.9,'HIGH',if(random()>0.2,'MOD','LOW')) |
#> select PN,value
#>
#>
#> $phecode_case_255.21
#> ── Phenotype ───────────────────────────────────────────────────────────────────
#> $name: phecode_case_255.21
#> $description: Glucocorticoid deficiency
#> $result_type: SET
#> $tag_list: Cases, Phecode
#> $pn_count: 439
#> $query:The results come back as a vector of phenotypes
We initialize an empty phenotype matrix object by running
get_phenotype_matrix optionally passing the
basevariable.
pheno_matrix <- get_phenotype_matrix()
class(pheno_matrix)
#> [1] "phenotype_matrix"
pheno_matrix
#> $base
#> NULL
#>
#> $phenotypes
#> list()
#>
#> attr(,"class")
#> [1] "phenotype_matrix"To add phenotype/s to the phenotype matrix we use either
phemat_add_phenotype for adding a single phenotype to the
matrix or phemat_add_phenotypes to add multiples. Note that
these methods do not have the same input arguments *
phemat_add_phenotype(name, phenotype_matrix, missing_value, label) *
phemat_add_phenotypes(names, phenotype_matrix, missing_value)
pheno_matrix <- phemat_add_phenotypes(phenos_subset_names, pheno_matrix, missing_value = '-99') #> [1] "pheno_matrix class: phenotype_matrix"
#> [1] "pheno_matrix content: base, phenotypes"
#> [1] "pheno_matrix phenotypes: phecode_case_253.3, phecode_case_255.21, phecode_case_255.11, phecode_case_253.11, phecode_case_180.3"
rm_pheno <- names(pheno_matrix$phenotypes)[1]
paste("Removing phenotype:", rm_pheno)
#> [1] "Removing phenotype: phecode_case_253.3"
pheno_matrix <- phemat_remove_phenotype(name=rm_pheno, pheno_matrix)
pheno_matrix
#> $base
#> NULL
#>
#> $phenotypes
#> $phenotypes$phecode_case_255.21
#> $phenotypes$phecode_case_255.21$name
#> [1] "phecode_case_255.21"
#>
#> $phenotypes$phecode_case_255.21$missing_value
#> [1] "-99"
#>
#> $phenotypes$phecode_case_255.21$label
#> [1] "phecode_case_255.21"
#>
#>
#> $phenotypes$phecode_case_255.11
#> $phenotypes$phecode_case_255.11$name
#> [1] "phecode_case_255.11"
#>
#> $phenotypes$phecode_case_255.11$missing_value
#> [1] "-99"
#>
#> $phenotypes$phecode_case_255.11$label
#> [1] "phecode_case_255.11"
#>
#>
#> $phenotypes$phecode_case_253.11
#> $phenotypes$phecode_case_253.11$name
#> [1] "phecode_case_253.11"
#>
#> $phenotypes$phecode_case_253.11$missing_value
#> [1] "-99"
#>
#> $phenotypes$phecode_case_253.11$label
#> [1] "phecode_case_253.11"
#>
#>
#> $phenotypes$phecode_case_180.3
#> $phenotypes$phecode_case_180.3$name
#> [1] "phecode_case_180.3"
#>
#> $phenotypes$phecode_case_180.3$missing_value
#> [1] "-99"
#>
#> $phenotypes$phecode_case_180.3$label
#> [1] "phecode_case_180.3"
#>
#>
#>
#> attr(,"class")
#> [1] "phenotype_matrix"Lastly, let’s fetch a phenotype from the project. We’ll use the first listed
pheno_data <- get_data(pheno_matrix, conn)
print(pheno_data)
#> # A tibble: 2,692 × 5
#> pn phecode_case_255.… phecode_case_25… phecode_case_25… phecode_case_18…
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1002261 -99 -99 -99 1
#> 2 1005939 -99 -99 -99 1
#> 3 1006278 1 -99 -99 -99
#> 4 1010500 -99 -99 -99 1
#> 5 1011704 -99 -99 -99 1
#> 6 1012415 1 -99 -99 -99
#> 7 1012521 -99 -99 -99 1
#> 8 1012811 -99 -99 -99 1
#> 9 1018263 1 -99 -99 -99
#> 10 1022873 -99 -99 -99 1
#> # … with 2,682 more rows