GOR-R

See full documentation at https://wuxi-nextcode.github.io/gorr/

Installation

Installing from github using devtools 

devtools::install_github("wuxi-nextcode/gorr")

Example

In this example, we’re going to run a simple remote gor query using the gorr package.

Load packages

First load the gorr package, the tidyverse package is recommended in general, but not required for this example

Connect to a Genuity Science APIs

First we’ll need to establish a connection to our APIs. To do that we’ll need to call platform_connect and provide it with the relevant parameters pointing to the api-services, i.e. api_key and project. You get the API Key from the /api-key-service/token endpoint on your CSA instance, and you can find a list of projects available to you in CSA as well, remember to use the internal project name. Once you have those two things, you have everything you need to make a connection object:

api_key <- "your_api_key_here"
conn <- platform_connect(api_key, project = "test_proj")

Alternatively, one can define the environment variables GOR_API_KEY and (optionally) GOR_API_PROJECT. This can also be done for your R session exclusively by including those lines in a .Renviron file in your working directory. When these variables are set you can call platform_connect without specifying api_key or project:

conn <- platform_connect() # uses both GOR_API_KEY and GOR_API_PROJECT
conn <- platform_connect(project = "test_proj") # uses only GOR_API_KEY

If everything goes as planned, we’ll have a conn object ready to be passed into the gor_query and other functions interacting with the APIs.

Gor Query Services

Now gor_query can be used to run a gor query:

result <- gor_query("gor #dbsnp# | top 100", conn)

The results come back as an R data.frame:

print(result)

By default, character columns are returned as is, i.e. not converted to factors. This allows for more flexibility on the user-end, but as an example here we an turn all character columns into factors:

result <- 
    result %>%
    mutate_if(is.character, factor)

It should also be noted that larger gor queries can be constructed inside a string block and piped directly into the gor_query function:

chr21_results <- "
  gor -p chr21 #dbsnp# 
    | where len(reference)=1 and len(allele)=1
    | calc snptype reference+'/'+allele 
    | hide rsIDs
    | top 100" %>%
    gor_query(conn)
print(chr21_results)

Phenotype Catalog Services

We can list available phenotypes in the phenotype-catalog using get_phenotypes and pass the connection object as before:

If the goal is to work with a single phenotype we can either fetch an existing phenotype from the catalog by passing the name of the phenotype and the connection object to the get_phenotype function or by creating a new phenotype using create_phenotype method. In both cases a phenotype object is returned containing the phenotype’s attributes.

phenotype <- create_phenotype(name, conn)

For more details on how to interact with the Phenotype Catalog Services, please see articles: Phenotype services, Phenotype matrix services, Phenotype categories and Phenotype playlists.